Multi-atlas pancreas segmentation: Atlas selection based on vessel structure

Ken’ichi Karasawaa,∗, Masahiro Odaa, Takayuki Kitasakab, Kazunari Misawac, Michitaka Fujiwarad,Chengwen Chue, Guoyan Zhenge, Daniel Rueckertf, Kensaku Morig,∗

aGraduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, 464-8601, JapanbSchool of Information Science, Aichi Institute of Technology, Yagusa-cho, Toyota, Aichi, 470-0356, Japan

cAichi Cancer Center, Chikusa-ku, Nagoya, Aichi, 464-8681, JapandNagoya University Graduate School of Medicine, Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan

eInstitute for Surgical Technology and Biomechanics (ISTB), University of Bern, Stauffacherstrasse 78, Bern 3014, SwitzerlandfDepartment of Computing, Imperial College London, SW7 2AZ, UK

gInformation and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, 464-8601, Japan

Abstract

Automated organ segmentation from medical images is an indispensable component for clinical applications suchas computer-aided diagnosis (CAD) and computer-assisted surgery (CAS). We utilize a multi-atlas segmentationscheme, which has recently been used in different approaches in the literature to achieve more accurate and robustsegmentation of anatomical structures in computed tomography (CT) volume data. Among abdominal organs, thepancreas has large inter-patient variability in its position, size and shape. Moreover, the CT intensity of the pancreasclosely resembles adjacent tissues, rendering its segmentation a challenging task. Due to this, conventional intensity-based atlas selection for pancreas segmentation often fails to select atlases that are similar in pancreas position andshape to those of the unlabeled target volume. In this paper, we propose a new atlas selection strategy based onvessel structure around the pancreatic tissue and demonstrate its application to a multi-atlas pancreas segmentation.Our method utilizes vessel structure around the pancreas to select atlases with high pancreatic resemblance to theunlabeled volume. Also, we investigate two types of applications of the vessel structure information to the atlasselection. Our segmentations were evaluated on 150 abdominal contrast-enhanced CT volumes. The experimentalresults showed that our approach can segment the pancreas with an average Jaccard index of 66.3% and an averageDice overlap coefficient of 78.5%.

Keywords: multi-atlas, pancreas segmentation, atlas selection, vessel structure, CT image

1. Introduction

Automated abdominal organ segmentation fromthree-dimensional medical images is one of the essentialcomponents for clinical applications, such as computer-aided diagnosis (CAD) (Kobatake (2007)), computer-assisted surgery (CAS) planning and navigation (Aza-gury et al. (2012)). Pancreatic cancer has recently beenincreasing globally (Ito et al. (2013)), and its mortalityrate is the fourth among cancer-related deaths in Japan,

2011 (Matsuda et al. (2011)). Because pancreatic can-cer is difficult to treat and has such a high mortalityrate, it would be beneficial to develop systems to au-tomatically detect the pancreatic region for radiologists(Shimizu et al. (2010)).

Among the abdominal organs, the pancreas has par-ticularly large inter-subject variability in its position, sizeand shape among the population. Also, the pancreashas a thinner shape in the abdominal region compared

∗Corresponding author. Tel: +81-52-789-5688Email addresses: kkarasawa@mori.m.is.nagoya-u.ac.jp (Ken’ichi Karasawa), kensaku@is.nagoya-u.ac.jp (Kensaku Mori)URL: http://www.mori.m.is.nagoya-u.ac.jp/wiki/ (Kensaku Mori)

Preprint submitted to ******************** March 28, 2017

with other abdominal organs. The CT intensity of thepancreatic region is very similar to its neighboring struc-tures, i.e. the stomach wall, duodenum and small/largeintestines. These properties render the segmentation ofthe pancreas relatively challenging.

1.1. Related works

In recent years, many organ segmentation methodshave been proposed (Summers (2016)). Among them,multi-atlas segmentation schemes have been widely uti-lized for abdominal organ segmentation (Iglesias andSabuncu (2015)). Early explorations of multi-atlas seg-mentation are reported for brain tissues from magneticresonance (MR) images (Rohlfing et al. (2004); Hecke-mann et al. (2006); Lotjonen et al. (2010)). Commonto all these methods is that they all adopt certain typeof global registration, e.g. affine registration, to obtainatlases to a target image space. Aljabar et al. (2009)demonstrated that atlas selection was effective in im-provement of segmentation accuracy for brain MR im-ages. Wolz et al. (2013) applied the locally weighted atlasselection scheme to multiple abdominal organ segmenta-tion from contrast-enhanced CT images. This approachpresents a free-form deformation (FFD) (Rueckert et al.(1999)) based hierarchical multi-atlas registration andsegmentation framework. Chu et al. (2013) performsMarkov random field (MRF)-based registration (Glockeret al. (2008)) and globally spatially divided hierarchicalconstruction of probabilistic atlas for abdominal multi-organ segmentation.

These multi-atlas approaches for abdominal organsegmentation have achieved good segmentation perfor-mances on relatively large organs, e.g. the liver. How-ever, as for the segmentation accuracy of the pancreas, itis still significantly lower than other abdominal organs.One of the main reasons is that the selection of atlaseswith high similarity in the pancreatic region tends to faildue to the fact that the tissues surrounding the pancreasregion have similar CT intensity. Also, the pancreaticshape and position have large inter-subject variability.These pancreatic properties cause the atlas-to-target reg-istration to be trapped in local minima. Wang et al.(2014) uses patch-based segmentation with a label fu-sion scheme using intensity and spatial context. Tonget al. (2015) also uses voxel-wise local atlas selection totrain a patch dictionary. However, these types of patchselections also consider only intensity similarity between

image patches, and therefore have a high risk of mis-selection of similar patches as shown in Bai et al. (2015).

1.2. Contribution of this paper

In this paper, we propose a new atlas selection strat-egy based on vessel structure to overcome the aforemen-tioned atlas selection failure and demonstrate its appli-cation to multi-atlas pancreas segmentation. The mainmotivation of utilizing vessel structure for the atlas se-lection is that each abdominal organ is generally close tocertain vessels, e.g. the pancreatic body is running alongthe splenic vein and the head is along the superior mesen-teric vein. A schematic illustration of anatomical struc-ture surrounding the pancreas is shown in Fig. 1. Differ-ent from vessels inside the brain region, these vessels havelarger diameters and can be utilized for organ positioninformation. For example, Farag et al. (2014a) consid-ers detecting three abdominal structures (splenic vein,superior mesenteric vein, and their confluence point) aslandmarks for pancreas localization. Other SSM-basedapproaches with anatomical landmark detection of or-gans and vessels (Shimizu et al. (2010); Hammon et al.(2013); Okada et al. (2015)) demonstrate the combina-tion of SSM, and these anatomical landmarks could bemore effective for the target segmentation.

The main contribution of this paper is summarizedas follows: We propose a method that uses vasculaturearound the pancreas in the atlas selection. This proce-dure is intended to reduce the mis-selection of similaratlas caused by CT intensity. To perform this, we intro-duce two types of applications of vessel structure. Atlasesare selected based on vessel structure image. One appli-cation utilizes vessel structure binarized image and theother utilizes vessel structure enhanced image. In thelight of the criterion in atlas selection, there are manyways for atlas selection. For example, Aljabar selectedatlases based on age (Aljabar et al. (2009)). Cordier se-lected atlases using intensity distribution (Cordier et al.(2015)). The paper by Iglesias and Sabuncu (Iglesiasand Sabuncu (2015)) surveys atlas selection criteria. Interms of atlas selection criteria, the proposed method canbe classified as the method utilizing anatomical structureinformation (including the splenic vein) in atlas selection.

In Section 2, we explain our multi-atlas segmentationpipeline in detail. In Section 3, CT images used in theexperiments and segmentation results are described. Wediscuss advantages and disadvantages of our method in

2

Figure 1: Schematic illustration of anatomical structure surrounding the pancreas.

Section 4 as well as comparison with other state-of-the-art approaches. This paper is concluded in Section 5.

2. Method

2.1. Overview

We briefly explain our pancreas segmentation methodstep-by-step (Fig. 2). First, spatial standardization ofthe abdominal region and extraction of the pancreaticregion are performed for all available atlas data. Eachatlas data includes CT volume and its manually labeledabdominal organs (liver, spleen, pancreas, and both kid-neys). Then, the vessel structure around the pancreasregion is automatically detected and vessel structure im-ages are generated. After that, atlas-to-target registra-tion is carried out between cropped pancreatic regions.The vessel structure images of each atlas data and itsassociated CT volume are warped with the resulting de-formation fields. Here, we have two types of vessel struc-ture images. One is a binarized image whose voxels have1 in the vessel region and others 0. The other is an en-hanced image whose voxels have a positive enhancementvalue in the vessel region and others 0. Then, we selectatlas data based on similarity between the deformed ves-sel images. Using the selected atlases, a probability mapof each target of the unlabeled CT volume is generated.

A graph cut optimization is performed to combine prob-ability and intensity prior of each organ. The input toour pipeline is an unlabeled CT volume and the outputis the labeled image of that CT volume.

2.2. Atlas data preparation

2.2.1. Spatial standardization of abdominal region

First, spatial standardization of all atlas data is ex-ecuted as preprocessing. The spatial standardization isuseful to reduce the inter-subject variability of positionsand sizes of abdominal organs. This process consists ofsimple image processing such as translation and scalingof the abdomen. The detailed process is shown in Chuet al. (2013) and a short summary is presented in Ap-pendix A. This process produces normalized atlas datawhose image size and resolution are the same in all theatlas data.

2.2.2. Pancreas VOI computation

We compute the volume of interest that contains thewhole pancreas region in each atlas data. We call thisvolume of interest the PVOI (pancreas volume of inter-est). PVOI is defined as a cuboid region whose edgesizes are all the same. PVOI size is defined as a maxi-mum size that covers all the pancreas regions stored inall atlas data. The center of a PVOI is computed fromthe positional information of the liver. First, we compute

3

Atlas data Input volume

1. Spatial Standardization of abdominal region

2. Pancreas VOI computation

5. Coarse-to-fine

segmentation

3. Vessel region extraction & Atlas-to-target registration

Final output

L

H

4. Atlas selection based on vessel structure

pancreas

vessel region

liver

spleen

kidneys

Probability maps of

abdominal organs

Figure 2: Proposed pipeline of multi-atlas pancreas segmentation. Left and right side indicate the process of atlas data set and unlabeledinput CT volume, respectively.

the liver and the pancreas centers for each atlas from theliver and the pancreas regions stored in the atlas dataset.These centers can be computed as the geometrical aver-age of voxel positions of each region. Then, we computethe average vector from the liver center to the pancreascenter as

∆ =1

N

N∑n=1

{O(n)P −O

(n)L }, (1)

where N is the number of atlas images stored in the

dataset, and O(n)P and O

(n)L are the positions of the pan-

creas and the liver. The PVOI center O(n)P is estimated

by the following equation:

O(n)P ≡ O

(n)L +∆ ≈ O

(n)P . (2)

The PVOI radius r is determined so that PVOIs with asize of 2r cover all the pancreas regions for all the atlasdata. The PVOI can be expressed as

R = {x | |x− OPx | ≤ r, (3)

|y − OPy | ≤ r,

|z − OPz | ≤ r }.

As stated above, a PVOI can be obtained from the livercenter information. Liver regions can be stably obtained

4

2r [voxel]

2r [voxel]

2r [voxel]

R [mm]

Average offset vector

between liver and pancreas

of ground truth ∆Liver center of

ground truth OL Estimated pancreas

center OP

r

r

rr

r

r

_

~

Figure 3: Illustration of pancreas VOI computation. Using liver center of ground truth OL and average offset vector ∆, the pancreascenter is estimated as OP . Then pancreas VOI centered at OP is extracted as above.

manually from a contrasted CT volume and are locatednext to the pancreas. Since the PVOI must also be ob-tained for an input CT data, this approach can easilyidentify a PVOI of an input data from the liver infor-mation stably obtained from an input data. Figure 3represents a graphical illustration of the PVOI computa-tion.

2.3. Vessel region extraction

This step computes vessel regions within each ex-tracted PVOI. The vessel structure images are gener-ated for all CT images including the atlas and input im-ages. The target vessel regions include the splenic vein(SV), superior mesenteric vein (SMV), aorta (AO), infe-rior vena cava (IVC), portal vein (PV). Below, we pro-pose two types of vessel images for atlas selection.

2.3.1. Vessel structure binarized image (VBI)

A vessel structure binarized image (VBI) is computedwithin a PVOI. In this image, voxels of the vessel regionare expressed as 1 and others are 0. Therefore, the vesselstructure binarized image is given by

VB(x) =

{1, if x is a vessel voxel,0, otherwise.

(4)

Figure 4 (a) shows an example of the automatically ex-tracted vessel structure binarized image in a transverseslice. Our procedure to generate the vessel structure bi-narized image is decomposed into the following two steps:AO detection and SV detection.

A set of AO seed points is explored in a transverseslice first. This is simply found by applying a morpho-logical operator, thresholding, and region labeling. Alabeled region with maximum area and roundness is se-lected as AO seed points. Region growing is performedfrom the seed points with a spherical component to de-tect AO voxels.

Next, SV detection begins with searching for thehilum point of the spleen using the ground truth of thespleen. We generate the ground truth of the spleen ofeach CT image by manual tracing. This point plays arole in the connection between the spleen and the SV.To detect the hilum point, we utilize a multi-scale lineenhancement filter with Hessian analysis detailed in Satoet al. (1998). A set of detected voxels with high enhance-ment values inside the spleen are selected as seed pointsof region growing. Again, region growing is performedwith a similar setting to earlier to detect SV voxels fromthe hilum via SV to the end of SMV.

Finally, we integrate the outputs of the AO detec-tion and the SV detection to generate a vessel structure

5

(a) (b)

Figure 4: (a) Automatically extracted vessel structure binarized image overlapped with its CT slice. The vessel region is colored red.(b) Vessel structure enhanced image. Color code indicates a low enhancement with blue and high enhancement with red.

binarized image VB(x).

2.3.2. Vessel structure enhanced image (VEI)

A vessel structure enhanced image represents vesselvoxels within a PVOI as a positive enhancement value,and the others as 0. To generate the vessel structure en-hanced image, we employ a multi-scale line enhancementfilter (Sato et al. (1998)). We utilize the vessel structurebinarized image VB(x) as a masking region to enhancethe vessel-like structures within it. The vessel structureenhanced image is given by

VE(x) =

w · max1≤i≤m

{σ2i · λ123(x;σi)}, if eT1 uz ≤ τ,

max1≤i≤m

{σ2i · λ123(x;σi)}, otherwise.

(5)

Here σi ≡ i·σ1 and σ1, λ123(·),m represent a standard de-viation of Gaussian function, output value of multi-scaleline enhancement filter, and step size of the enhance-ment filter, respectively. These notations are employedin the conventional multi-scale line enhancement filter(Sato et al. (1998)). The λ123(·) is give as

λ123(x;σi) =

|λ2|+ λ1, if λ3 < λ2 < λ1 ≤ 0,

|λ2| − λ1,if λ3 < λ2 < 0

< λ1 < |λ2|,0, otherwise.

(6)

λ1, λ2, and λ3 represent the first, second, and third eigen-value of the Hessian matrix at x with a σi, respectively.

We introduce the following four new mathematicalsymbols into the conventional multi-scale line enhance-ment filter: w, e1,uz, τ in Eq. (5) are a weight coeffi-cient, unit vector of the first eigenvector of the Hessianmatrix centered at x with scale of σi, unit vector of thehead-to-foot direction, and threshold value, respectively.This paper utilizes the anatomical knowledge that theSV running along the pancreatic region is basically or-thogonal to the head-to-foot axis, where the formed angle≥ θconst. The weight w controls the strength to empha-size the SV. Figure 4 (b) shows an example of the vesselstructure enhanced image obtained by applying Eq. (5).Figure 5 shows 3D visualizations of the vessel structureenhanced images.

2.4. Atlas-to-target registration

Each atlas PVOI is aligned to the unlabeled PVOIwith the nonrigid registration technique based on Markovrandom field (MRF) optimisation (Glocker et al. (2008)).After the MRF optimization, each atlas PVOI includingthe vessel images VB(x), VE(x) is warped using the de-formation field. The atlas-to-target registration is per-formed within the whole PVOI.

6

Figure 5: 3D visualizations of vessel structure enhanced images by the volume rendering technique. Regions having high vessel enhancedvalues are displayed as opaque region.

2.5. Atlas selection based on vessel structure image

A similarity between the vessel images of each at-las and unlabeled CT is measured, and atlases withthe Nh highest similarities are selected. Each selectedatlas is weighted with that similarity and averaged togenerate a probability map of each organ Ml(x), wherel ∈ {liver, spleen,pancreas, both kidneys, background}.

We propose two types of atlas selection schemes basedon different image similarity measurements of the ves-sel structure. One utilizes the Jaccard index (Jaccard(1901)), which assesses an overlap between vessel regionsof an input PVOI and an atlas PVOI.

Another utilizes normalized cross correlation (NCC)(Bracewell (1965)). NCC also measures a similarity be-tween vessel regions of an input PVOI and an atlasPVOI. NCC places much importance on the vessel-likestructures with the line enhancement value.

2.6. Pancreas region segmentation

Coarse-to-fine pancreas segmentation using the prob-ability maps Ml(x) is performed. This process combinesmaximum a posterior (MAP) estimation (Levitan andHerman (1987)) and refinement with multi-class graphcut optimization (Boykov et al. (2001); Boykov and Kol-mogorov (2004)).

We use an expectation maximization (EM) algorithm(Dempster et al. (1977)) to estimate a probability den-sity function P (I(x)|θl) of CT intensity of each organ lfor voxel x within the non-zero region of Ml(x), exceptfor a background. Here, θl indicates a set of parameters

that the EM algorithm infers for organ l. Each organregion is modeled as a mixture of three Gaussian dis-tributions. Each distribution represents a low-intensity,high-intensity, and organ region, respectively. A low-intensity region mainly includes the air and fat tissueswhile the high-intensity region includes vessel and boneregions. This is helpful in order to exclude the non-organregions included in the probability map and makes eachestimation more accurate. The MAP estimation is com-puted by the following formula of

l∗(I(x)) ≈ argmaxl

P (I(x)|θl)Ml(x), (7)

where l∗(I(x)) represents an estimated optimal organ la-bel of voxel x with intensity value of I(x).

The estimation is done by multi-class graph cut opti-mization in the same manner as Wolz et al. (2013) (Ap-pendix B).

3. Data and Results

3.1. Material

150 abdominal CT scans acquired from 36 female and114 male subjects were used for the experiments. Allscans were acquired between 2004 and 2009 at NagoyaUniversity hospital by a TOSHIBA Aquilion 64 scannerand obtained under typical clinical protocols for the pur-pose of laparoscopic resection of the stomach and gall-bladder glands or colon. 141 subjects had early or ad-

7

Table 1: CT specification.

Slice size [pixels] 512 × 512Slice number [slices] 263 ∼ 1061Pixel spacing [mm] 0.546 ∼ 0.820Slice thickness [mm] 0.400 ∼ 0.800

Table 2: Acquisition parameters.

X-ray tube voltage [kV] 120X-ray tube current [mAs] 350 ∼ 400

Starting point(patient age < 60) 25s delayed after injection

Starting point(patient age ≥ 60)

7s after intensity of aortais over 80 H.U.

00.2

0.4

0.6

0.8

1

Overlap m

etr

ic

Box plots

JI DSC

********

***

(a)

05

10

15

Box plots

ASD RMSD

Surf

ace e

rror

[mm

]

(b)

Figure 6: Box plots of four evaluation metrics of the proposed segmentation and previous one (Karasawa et al. (2015)). From left toright, proposed segmentation based on VBI, VEI, and previous one in each column. A black horizontal line of each box plot indicatesa median value. ** and *** represent that there exists a significant difference (p < 0.01) and stronger significant difference (p < 0.001)between two methods, respectively. Bottom value of each box plot represents average ± standard deviation. (a) Box plots of Jaccardindex and Dice coefficient. (b) Box plots of average surface distance and root-mean square distance.

vanced gastric cancer, one subject had cholecystitis can-cer and eight subjects had colorectal cancer. All subjectswere aged between 26 and 84 years with a mean age of62.8± 12.0. CT specification and acquisition parametersare summarized as Tables 1 and 2.

Manual rating was performed for the ground truthdata of the liver, spleen, pancreas and the kidneys. All150 subjects were segmented by one out of three trainedraters. Segmentation of the ground truth images is basedon interactive region growing, where a spherical elementis utilized to prevent excess segmentation of a target re-gion, or graph-cut segmentation, where a set of fore-ground and background voxels is manually set as seedpoints. After the semi-automated segmentation, manualcorrection of all the segmentations was performed on allthe slices of right-to-left, ventral-to-back, and head-to-foot directions.

In the experiments shown in this paper, we utilizedliver regions manually traced. This trace can be replacedwith automated method such as the one developed byWolz et al. (2013). The center is computed from theliver region.

Four types of measurements are used for segmenta-tion evaluation: Jaccard index (JI) (Jaccard (1901)),DSC similarity coefficient (DSC) (Dice (1945)), averagesymmetric surface distance (ASD), and root-mean aver-age symmetric surface distance (RMSD) (Heimann et al.(2009)). JI and DSC measurements evaluate the overlapbetween the ground truth and the segmentation resultof the pancreas. The others evaluate a surface distanceerror between them.

We fixed the parameters as r = 84, w = 2.0, τ =0.25, σ1 = 1.0,m = 10. These values were experimen-tally determined. We performed the segmentation ex-

8

(a) (b) (c)

(d) (e) (f)

Likelihood

High Low

Segmentation

boundaryGround truth

boundary

Figure 7: Example where the segmentation accuracy is improved by proposed method compared to the previous one. Each imagerepresents a coronal slice in the same position. From left to right, a probability map and the corresponding segmentation contouroverlapped with the ground truth contour of the conventional, VBI-based, and VEI-based methods, respectively. (a) - (c) indicate theprobability maps. Color code represents low probability with blue and high probability with red. (d) - (f) indicate the correspondingsegmentation contours and ground truth contours. Ground truth boundary is traced in yellow and segmentation boundary in red.Segmentation performance is 34.2%, 76.9%, and 80.2% with respect to JI, respectively.

periments using the atlas selection with Nh = 20. Thisnumber is selected based on previous experiences.

3.2. Experimental results

Leave-one-out cross validation was utilized for theperformance evaluation. All experiments were executedon servers equipped with an Intel Xeon Dual or QuadCore 1.86 ∼ 3.07 GHz CPU. The overall runtime per CTvolume takes between two ∼ four hours.

Two types of the atlas selections described in Section2.5 are performed separately and evaluated. For simplic-ity, we refer to vessel structure binarized image as VBIand vessel structure enhanced image as VEI.

Figure 6 (a) and (b) show box plots of the evalua-tion metrics of the proposed segmentation and the con-ventional one (Karasawa et al. (2015)), respectively. InFig. 6, both VBI- and VEI-based segmentation muchoutperform the conventional one in terms of JI and DSCmeasurements. We performed a statistical pair-wise two-tailed t-test in terms of JI, DSC, ASD, and RMSD. Inthe t-test in terms of JI and DSC between the VBI-basedmethod and the conventional method, the VBI-basedsegmentations were significant with p = 2.26 × 10−4 <0.001, 1.25 × 10−3 < 0.01, respectively. In the t-test interms of JI and DSC between the VEI-based method andthe conventional method, the VEI-based segmentations

9

(a) (b) (c)

(d) (e) (f)

Likelihood

High Low

Segmentation

boundaryGround truth

boundary

Figure 8: Example where the segmentation accuracy is reduced by the proposed method compared to the previous one. Each imagerepresents a transverse slice in the same position. From left to right, a probability map and the corresponding segmentation contouroverlapped with the ground truth contour of the conventional, VBI-based, and VEI-based methods, respectively. (a) - (c) indicate theprobability maps. Color code represents low probability with blue and high probability with red. (d) - (f) indicate the correspondingsegmentation contours and ground truth contours. Ground truth boundary is traced in yellow and segmentation boundary in red.Segmentation performance is 64.0%, 29.3%, and 31.1% with respect to JI, respectively.

were significant with p = 5.74 × 10−9, 2.96 × 10−7 <0.001, respectively. As shown in Fig. 6 (b), there is nostatistical significance between the proposed and the pre-vious methods on the surface error measurements. Thereason for this will be discussed in Section 4.

Figure 7 shows an improved case with the probabilis-tic map and the corresponding segmentation contour to-gether with the ground truth contour. Figure 7 (a) -(c) show the probabilistic map generated by the conven-tional, VBI-based, and VEI-based methods, respectively.Figure 7 (d) - (f) show the contours of the ground truthand the segmentation result of the conventional, VBI-based, and VEI-based methods, respectively. From left

to right, segmentation performance is 34.2%, 76.9%, and80.2% with respect to JI, respectively. The white arrowsin (a) point to areas where the probability is relativelyhigher in the duodenum and small intestine regions withsimilar intensity to the pancreas. False positives to theseregions have occurred in (d). However, we can see in (b)and (c) that the probability of these regions is loweredby the proposed VBI- and VEI-based segmentation. Thisresulted in the elimination of the false positives.

Figure 8 shows a worse case with the probabilisticmap and the corresponding segmentation contour to-gether with the ground truth contour. Figure 8 (a) -(c) indicate the probabilistic map generated by the con-

10

020

40

60

80

100

10 20 30 40 50 60 70

[%]Average overlap between vessel regions

of selected atlas PVOIs and of input PVOI

Segm

enta

tion p

erf

orm

ance

[%]

Relation between vessel overlap and segmentation

Figure 9: Scatter plot that describes relation between vessel region overlap within a PVOI and pancreas segmentation accuracy. Theoverlap is measured with DSC. Horizontal axis represents average overlap between vessel regions of selected atlas PVOIs and of inputPVOI. Vertical axis represents average overlap between pancreas regions of selected atlas PVOIs and of input PVOI. Green arrowsshow that the higher the average overlap between vessel regions of selected atlas PVOIs and of input PVOI becomes, the smaller thedispersion of segmentation performance.

ventional, VBI-based, and VEI-based methods, respec-tively. Figure 8 (d) - (f) indicate the contours of theground truth and the segmentation result of the con-ventional, VBI-based, and VEI-based methods, respec-tively. From left to right, the segmentation performanceis 64.0%, 29.3%, and 31.1% with respect to JI, respec-tively. For this case, we can see that the probabilitymap has a better shape than the previous (a); however,the segmentation results of the proposed methods havefalse negatives in the pancreatic head region. This im-plies that the regularization term in the coarse-to-finesegmentation is important and should be automaticallyadjusted for every CT volume.

A scatter plot that describes a relation between vesselregion overlap within PVOI and pancreas segmentationaccuracy is shown in Fig. 9. Standard deviations of pan-creas centroid along x-, y-, and z-axes are shown in Fig.10. Examples of cases who have relatively large surfacedistance error (> 10 mm with ASD) are shown in Fig.11.

4. Discussion

Through our experiments using vessel structure-based atlas selection, we were able to obtain better per-formances on the pancreas segmentation. In particular,we obtained better segmentation performance using ves-sel structure enhancement, which places more emphasison the splenic vein region. From this reason, we can con-clude that the atlas selection based on vessel structureis much more effective in selecting atlases with similarpancreatic shape and position to that of an input CTvolume.

Figure 9 indicates a scatter plot that describes a re-lation between vessel region overlap and segmentationperformance. The vessel region overlap in the horizontalaxis represents an average overlap between vessel regionsof selected N atlas PVOIs and of input PVOI. The ver-tical axis represents the segmentation accuracy assessedwith DSC. From the plot, we can observe the follow-ing: one is that the vessel structure-based atlas selectionfor multi-atlas pancreas segmentation has a positive ef-

11

010

20

right-to-le

(x-axis)

ventral-to-back

(y-axis)

head-to-tail

(z-axis)

Original Regularized ROP

Sta

nd

ard

de

via

tio

n [%

]

15.4

7.84

9.91

15.3

6.357.91

16.217.1

11.1

Standard deviation in Cartesian coordinate system

Original Regularized PVOI

right-to-left

(x-axis)

ventral-to-back

(y-axis)

head-to-foot

(z-axis)

Figure 10: Standard deviations of pancreas centroid along x-, y-, and z-axes in original (blue), standardized (orange), and croppedPVOI space (green). Lower standard deviations are obtained in the x- and y-axes by the abdominal spatial regularization. The nextPVOI extraction reduces much standard deviation in the z-axis.

fect on selecting atlases with similar pancreas shape andposition. The other is that this atlas selection schemeis effective in elimination of the dispersion of the seg-mentation performance. As shown by the red regressionline in this figure, there exists a weak positive correlation(=0.126) between the average vessel overlap and the pan-creas segmentation performance. However, we can alsoobserve that the variability of segmentation accuracy isamplified in the low vessel overlap region while it getssmaller in the high vessel overlap region.

We performed the spatial standardization of the ab-dominal region to relieve the inter-subject variability ofthe body trunk size and position, followed by the PVOIextraction to localize the pancreatic region more pre-cisely. Figure 10 demonstrates that we were successfulin eliminating standard deviations in the x-, y-, and z-directions by the abdominal spatial regularization andPVOI extraction. Less variability of the pancreas posi-tion and shape in the PVOI contributes to reducing thelocal minima of the atlas-to-target image registration tothe pancreas region of an input CT volume. Therefore,this lower variability of the standard deviations in thethree directions makes the registration to the pancreasmore precise.

Table 3 shows a comparison with existing state-of-the-art works on pancreas segmentation performance.The first row shows the segmentation performance using

the VBI-based or VEI-based atlas selection. The sec-ond row shows the performance evaluated on the samedata set used in our experiments. The third row showsthe performance evaluated on a different data set fromours. This table shows the pancreas segmentation accu-racies. From this table, we can see that the proposedVBI- and VEI-based atlas selection approaches outper-form any other state-of-the-art work with respect to JIand DSC overlap. The experimental result of Jiang et al.(2013) outperforms ours; however, their method was onlyevaluated on 10 cases and requires manual input of seedpoints. In contrast, our segmentation pipeline is fullyautomated and requires no manual delineation.

This paper is focused on pancreas region segmenta-tion utilizing pancreas-specific atlas selection. Atlas se-lection was performed by considering splenetic arteries.Although the proposed method is tuned for pancreas seg-mentation, the similar idea can be utilized for segmenta-tion of the kidney, which is characterized by renal arter-ies.

Future works mainly include the following six points.While we obtained better segmentation performance interms of JI and DSC than most of the state-of-the-artmethods shown in Table 3, our segmentation pipelinestill has much higher computational burden comparedwith other approaches based on machine learning. Thispoint is a main disadvantage of our method to be im-

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ASD = 22.0 mm

JI = 15.7 %

(a)

ASD = 25.1 mm

JI = 11.8 %

(b)

ASD = 18.7 mm

JI = 31.1 %

(c)

ASD = 12.3 mm

JI = 40.9 %

(d)

ASD = 23.9 mm

JI = 24.0 %

(e)

ASD = 17.3 mm

JI = 24.4 %

(f)

Ground truth SegmentationG S

Figure 11: This figure shows six cases who have relatively large surface distance error (> 10 mm with ASD) compared with regionoverlap metric. Each figure indicates a ground truth (blue) and its segmentation result (red) whose segmented region only has a part ofthe pancreas. These segmentation results become a main cause of the deterioration of the surface distance error metric like ASD andRMSD.

proved in the future. This is mainly caused by the atlas-to-target nonrigid registration. The computational com-plexity can be an issue in the application to the CADsystem of pancreatic cancer, considering surgeons maywish to diagnose the cancer on the spot.

We used an in-house dataset of CT volumes in ourevaluation. To compare the segmentation accuracy withthe previous methods, use of the same dataset is impor-tant. We plan to evaluate our method on available opendatasets on which previous methods have been evaluated.

Figure 11 shows examples which have relatively largesurface distance error (> 10 mm with ASD). As shownhere, with our method there were cases whose surfacedistance errors were more deteriorated than the over-lap measurements. This indicates that atlases selectedby the proposed method failed to represent the pancreas

shape in some cases. Shape robustness of atlas need to beimproved. Incorporating a shape prior or shape conditionusing an organ statistical shape model into the smooth-ness term of the graph cut of the proposed method willallow us to consider the correspondence between two pan-creas parts, e.g. the pancreas tails, of the pancreas statis-tical shape model and of the pancreas region of an inputCT volume. Also, the performance measures such as JI,DSC, ASD, RMSD, or others are needed to be selectedby clinical application demands. The proposed methodmay have satisfying performance for some applications.

Throughout the experiments, the atlas selection num-ber Nh = 20 is fixed. Other atlas selection numbers, e.g.30, should be tested to explore the impact on the seg-mentation performance. Also, we need to automaticallydetermine an optimal number for every input CT volume.

13

Table 3: Comparison table of pancreas segmentation from CT scan. First row shows proposed methods, second row shows segmentationperformance of other groups using the same dataset as ours, and third row shows the segmentation performance of other groups using adifferent dataset from ours. The table only shows the segmentation performance (JI, DSC, ASD) on the pancreas. We use the followingbrief symbols for each organ: heart (H), stomach (ST), esophagus (E), liver (L), spleen (S), pancreas (P), kidneys (RK, LK), gallbladder(G), portal vein (PV), aorta (AO), inferior vena cava (IVC). Also, we use the following brief symbols for each CT phase: multiple phase(M), portal venous phase (P), arbitrary phase (A), non-contrast enhanced phase (N).

Method# ofdata

CT phaseSegmentation

targetRuntime[h/case]

Performance on pancreas segmentationJI [%] DSC [%] ASD [mm]

VBI base 150 P P 2 ∼ 4 63.9 ± 17.1 76.3 ± 16.4 2.53 ± 3.37VEI base 150 P P 2 ∼ 4 66.3 ± 15.5 78.5 ± 14.0 2.50 ± 4.01

Wolz et al. (2013) 150 P L,S,P,RK,LK 3 55.5 ± 17.1 69.6 ± 16.7 3.72 ± 4.36Chu et al. (2013) 100 P L,S,P,RK,LK - 54.6 ± 15.9 69.1 ± 15.3 1.88 ± 0.64Wang et al. (2014) 100 P L,S,P,RK,LK 14 - 65.5 ± 18.6 -

Karasawa et al. (2015) 150 P P - 60.5 ± 18.1 73.4 ± 17.6 3.16 ± 4.57Tong et al. (2015) 150 P L,S,P,RK,LK 0.5 ∼ 2 56.9 ± 15.2 71.1 ± 14.7 -

Kitasaka et al. (2008) 22 M P - visual assessment: good 12, normal 6, bad 4

Shimizu et al. (2007) 10 N

H,ST,EL,S,P,RK,LKG,PV,AO,ICV

- 32.5 - -

Shimizu et al. (2010) 98 M P 45 [min] 57.9 - -Hammon et al. (2013) 40 P P 20.4 [min] 61.2 ± 9.08 - 1.70 ± 0.71

Jiang et al. (2013) 10 P P0.195 ∼ 0.199

[s/slice] - 88.4 -

Farag et al. (2014b) 80 P P 2 ± 3 [min] 57.2 ± 25.4 68.8 ± 25.6 -

Roth et al. (2015) 82 P P55 (training)

1 ∼ 3 [min] (testing)- 68 ± 10 -

Roth et al. (2016) 82 P P 2 ∼ 3 [min] - 78.0 ± 8.2 0.60 ± 0.55

Okada et al. (2013) 87 AL,S,P,RK,LKG,AO,IVC

- 52.8 - -

Okada et al. (2015) 134 AL,S,P,RK,LKG,AO,IVC

- 60.4 ± 16.7 73.4 ± 15.1 2.77 ± 2.40

Saito et al. (2016) 140 M P,S 213 [s] 62.3 ± 19.5 74.4 ± 20.2 -

The VBI- and VEI-based atlas selection methods usethe Jaccard index (JI) and normalized cross correlation(NCC) as image similarity to select similar atlases fromthe data set. It is worth to explore other image similaritymetrics like normalized mutual information (NMI) andaverage surface distance (ASD).

We utilized the vessel structure information in thePVOI in the research and obtained the best segmenta-tion performance using the atlas selection based on it.However, we only focused on the vasculature around thepancreatic region. On this point, we should investigatethe effectiveness of the whole abdominal vessel structurewith a large field-of-view for similar atlas selection ofother abdominal organs, e.g. the liver. It’s also promis-

ing to use abdominal anatomical landmarks for similaratlas selection because they also have the anatomical in-formation that gives a prior of organ position.

Some processes in the proposed method utilize man-ually segmented regions of organs including liver andspleen. Also, the spatial standardization of abdominalregion process Chu et al. (2013) includes organ segmen-tation process. These segmentation results affect pan-creas segmentation accuracy of the proposed method.The relation between them need to be clarified. Fullautomation of these organ segmentation processes willbe necessary.

14

5. Conclusion

We proposed a multi-atlas pancreas segmentationmethod based on vessel structure around the pancreas.We also utilized the spatial standardization of the ab-dominal region and extraction of the pancreatic region(PVOI) to make the variability of the pancreas centroidsmaller, which contributes to the lower local minima ofthe image registration. From the experimental results,we achieved 66.3% average JI and 78.5% average DSCoverlap using the multi-atlas segmentation pipeline us-ing vessel structure enhanced image (VEI)-based atlasselection. This VEI emphasizes more on the splenic veinregion, which runs along the pancreatic region. We canconclude that the vessel structure-based atlas selection iseffective in selecting atlases with similar pancreas shapeand position to that of an input CT volume.

Acknowledgment

The authors would like to express gratitude to oursupervisors and colleagues for constructive suggestionsand advice. Parts of this research were supportedby the MEXT, the JSPS KAKENHI Grant Numbers25242047, 26108006, 26560255, JSPS Joint ResearchProjects “Turning medical image collection into anatom-ical knowledge wisdom based on image recognition andunderstanding and its application to computer-assisteddiagnosis and surgery”, and the Kayamori Foundation ofInformational Science Advancement.

Appendix A

Spatial standardization of the abdominal region issimply performed by image scaling and translation us-ing

x′ = xT1ST2, (8)

where T1 and T2 represent two translation matrices andS represents a scaling matrix. A voxel x in the originalspace is projected into a new position x′ in the standard-ized space.

For the purpose of finding the matrices, we utilizea body trunk region (which includes abdominal cavity,anatomical tissues inside the body, and body surface),lung regions, and kidney regions. These regions are ex-tracted with the combination of some image processing

techniques such as region growing and 26-neighbor la-beling. The kidney and the lung regions are roughly seg-mented by thresholding and connected component pro-cesses. Since we utilized the contrasted CT images inthe experiment, the kidney can be extracted by simplethresholding process. The lung regions also can be ex-tracted by thresholding and connected component. Wedefine the matrices as

T1 =

1 0 0 00 1 0 00 0 1 0

−Cx −Cy −Bz 1

, (9)

S =

ww 0 0 0

0 hh 0 0

0 0 dd 0

0 0 0 1

, (10)

T2 =

1 0 0 00 1 0 00 0 1 0Cx Cy Bz 1

, (11)

where w, h indicates an average width and height of thebounding box of the body trunk, respectively. Cx, Cy

indicates an average center position in the transverseplane. d is an average distance between the bottoms ofthe lungs and the kidneys. Bz is an average bottom po-sition of the lungs. These six values are computed aheadof the experiments. The unit of the values is millime-ters. For an unlabeled CT volume, the same six valuesof w, h,Cx, Cy, d, Bz are calculated in the same manner.

Appendix B

In the multi-class graph cut, we define an energy func-tion as

E(L) =∑x∈R

R(x) + λ∑

(x,x′)∈E

B(x,x′), (12)

where L shows an array of the label disposition estimatedby Eq. (7). λ is a regularized coefficient. R(·) and B(·, ·)are a data term and smoothness term, respectively. Thedata term R(·) represents a binary disagreement betweenthe estimated labels l∗x and each organ label l and is de-fined as

R(x) =

{0, if l = l∗x,1, otherwise.

(13)

15

The smoothness term B(·, ·) measures a penalty of dis-continuity between voxels and is given as

B(x,x′) =

{0, if lx = lx′ ,1

(1+|I(x)−I(x′)|)·d(x,x′) , otherwise,(14)

where E represents a set of neighboring voxels definedas E = {(x,x′)|x ∈ R,x′ ∈ N x}. N x is a set of voxelsconnecting to x with 26-neighbor. The function d(·, ·)measures Euclidean distance between two voxels.

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