presentation for ph.d in 2006

Computer Aided Detection Algorithm for Lung Disease

using Multi-Detector CT

심사위원장 : 조 규 성 심 사 위 원 : 조 남 진

조 성 오 예 종 철 김 진 환

김 진 성Dept. of Nuclear and Quantum Eng. KAIST

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Contents

1. Introduction1. Computer Aided Diagnosis (CAD)2. CAD for Lung Disease3. Practical Problems

2. Objective and Scope of Work1. Objective and Scope of work2. Summary of Proposal

3. Solid Pulmonary Nodule Detection Algorithm

4. Ground Glass Opacity Detection Algorithm

5. Conclusion

Introduction Objective & Scope SPN Detection ConclusionGGO Detection

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Contents

1. Introduction1. Computer Aided Diagnosis (CAD)

1. What is CAD?2. Effectiveness of CAD3. Application of Various CAD

2. CAD for Lung Disease1. Lung Cancer Overview2. What is SPN & GGO?3. Research Trend

3. Practical Problems

2. Objective and Scope of Work



5. Further Study


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Computer Aided Diagnosis (CAD)

What is CAD?Computer-Aided DiagnosisComputer-Aided Detection Second opinion

Purpose of CADImprovement of diagnostic accuracy

–Overload : 300 images/patient for lung CT–Radiologist’s limitation : 45% sensitivity for 3mm nodule

Consistency of image interpretation–Difficulty for radiologist to maintain high alertness at all time

CAD is best defined as a method of assist ing radiologic interpretation by means of computer image analysis. Ideally, CAD results in improved decision-making and performance due to enhanced detection and evaluation of complex imaging features, decreased inter-observer variability, and elimination of otherwise repetitive of tedious tasks.

Jane Ko, Naidich DP, “Computer-aided diagnosis and the evaluation of lung disease”. J Thorac Imaging. 2004 Jul;19(3):136-55. Review.

1. Computer Aided Diagnosis2. CAD for Lung Disease3. Practical Problems


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Effectiveness of CAD

To study the effects of CAD system

Large observer Test22 lung nodules in 8 patients’ CT data sets202 observers at a national radiology meeting

–68 nonradiologists, 95 nonthoracic radiologists, 39 thoracic radiologistsCAD system alone: 86.4% detection rate

Detection rates before and after CAD

Size of Nodule Nonradiologist Nonthoracic Radiologist Thoracic Radiologist

Before CAD After CAD Before CAD After CAD Before CAD After CAD

≤ 4mm 55.2 77.6 57.9 86.8 71.4 89.3

> 4mm 78.5 88.2 75.3 90.3 81.0 89.4

Matthew S. Brown, J.G. Goldin, “Computer-aided lung nodule detection in CT: results of large-scale observer test”, Acad Radiol. 2005 Jun;12(6):681-6.



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Application of Various CAD

Breast cancerUse of Digital Radiography (Mammogram)

Fully commercialized: R2 (ImageChecker), etc…

Lung cancerUse of developed CT techniqueCommercialization is in progress by R2, Siemens, Phillips

Colon and rectum cancerHot issue : began in 2000

Liver, BrainFunctional MRI, Neuroscience



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CAD for Lung Disease

2004 년 사망자중 사망원인별

2004 년 암에 의한 사망구성비

사망자수 ( 명 ) 구성비 (%) 1 일 평균 사망자 수 ( 명 )

암 65,000 26.3 177

뇌혈관질환 34,000 13.9 93

심장질환 18,000 7.3 49

고의적자해 ( 자살 ) 12,000 4.8 32

당뇨병 12,000 4.8 32

암사망자구성비

폐암 위암 간암 대장암 췌장암 자궁암 전립선암 유방암 백혈병

1994 21.3 16.7 25.6 20.5 5.0 3.9 3.0 0.4 1.7 2.8

2004 26.3 20.6 17.4 16.9 9.1 4.7 2.1 1.4 2.3 1.7

증감 5.0 + 3.9 - 8.2 - 3.6 + 4.1 + 0.8 - 0.9 + 1.0 + 0.6 - 0.5

2004 년 사망원인 통계결과 – 통계청 (2005. 9. 발표 )



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What is characterization of SPN & GGO?

Solid Pulmonary Nodule Ground Glass Opacity

Shape1. Small, round or egg-shaped lesion2. Less than 3-4cm in diameter

1. Hazy lung opacity2. May be seen as diffuse or more often

patchy in distribution taking sometimes a geographic or mosaic pattern.

Importance

1. 40% of SPNs are malignant.2. Malignant SPNs may be primary Stage I

lung cancer tumors or metastases from other parts of body

1. Indicates the presence of an active and potentially treatable process; active disease is present in more than 80% of patients who show GGO.

CAD Research

1. Over 20 years research period 2. Good results with computing power and

new technology of multi-detector CT image

1. More difficult than SPN because their lower density

2. Early stage of research development with texture analysis with neural network



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Previous Algorithm for SPN Detection

Research Group Sensitivity False positive Contents

Armato SG(Med.Phys 2001)

70%(89%)

1.5 /image(1.3 /image)

9 features(2D, 3D)First researth using 2.5 D

Linear discriminant analysis

Jane Ko(Radiology 2001)

86% unspecified

location, shape, volumebased on time study

High detection error on vesselDetection error large SPN

Brown MS(Radiology 2002)

100%70%

15 /case3D shape information


Reeves AP(SPIE 2004)

95.7% 19.3 / caseOnly pleural nodule

3D search space, 3D connectionNot applied juxtavascular nodule

Lee JW(Invest Radiol 2004)

81% 28.8 / case

3D recursive analysisUse of Radial distribution

Performance dependency on locationDetection error large SPN

Practical ProblemsHigh detection error with vessel component

–Single detection algorithm for all nodulesHigh false positive rateDetection error with large solid pulmonary nodule



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Previous Algorithm for GGO Detection

Research Group Methods Results

Kim KGRadiology. 2005

2D CT image processing 30 x 30 ROI (50% overlap)

2D texture feature2 layer ANN

With a threshold of 0.9 (ANN)Sensitivity: 94.3% (280/297 ROIs)

0.89 false positive /imageDisplay the square ROI box

Uchiyama YMed Phys 2003

For diffuse lung disease32 x 32 (96 x 96) ROI analysis

3 layer ANN

Abnormal caseSensitivity: 99.2% (122/123 ROIs)Didn’t mention false positive rate Not exact volume (only existence)

Kauczor HUAJR 2000

Not given details6% classified as GGO of total

2nd classified 99% of GGOFalse positive: 24%

Practical ProblemSegmentation exact area or volume of GGOInput parameter dependency

–General 2D slice CT image & Texture only with ROI, Neural Networks (MLP)High false positive rateAlgorithm for only pure GGO


*ANN: Artificial Neural NetworkIntroduction Objective & Scope SPN Detection ConclusionGGO Detection

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Contents

1. Introduction

2. Objective and Scope of Work1. Objective and Scope of work2. Summary of Proposal



2. Further Study


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Objective & Scopes

MotivationSPN detection

–Modular algorithm for each types of nodules

–Reduction of detection error for juxtavascular noduleGGO detection

–3D CAD algorithm without artificial neural network

Objective

To f ind more powerful & New CAD algorithm

using 3D information of MDCT for early lung cancer

Scope of Work Solid Pulmonary Nodule (SPN) Detection Algorithm Ground Glass Opacity (GGO) Detection Algorithm


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Summary of Proposal

1. Computer Aided Diagnosis (CAD) is important to improve the accuracy and consistency of radiologists in medical imaging.

2. CAD for lung nodule is significant. CAD process for lung nodule is consist of automatic detection and classification.

3. For detection, proposed simple CAD algorithm showed a good preliminary results with modular design according to each type of nodule.

4. Proposed GGO detection algorithm will show more efficient method than other group.

Before Proposal Further Study

SPN Detection

Algorithm(40%)SPN CAD Evaluation (40%)

Verif ication of Algorithm(20%)

GGO Detection Algorithm (30%)

Supplement of Algorithm (20%)GGO CAD Evaluation (45%)

Verification of Algorithm (5%)


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Contents

1. Introduction


3. Solid Pulmonary Nodule Detection Algorithm1. Introduction2. Materials & Methods

1. SPN Detection algorithm development2. 3D Morphological matching algorithm

3. Results4. Conclusion


5. Conclusion


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Introduction

Practical ProblemsHigh detection error with vessel component

–Single detection algorithm for all nodulesHigh false positive rateDetection error with large solid pulmonary nodule

Modular Detection AlgorithmTwo detection CAD algorithm for 3 different types of SPN

–Isolated, pleural nodule – 3D shape analysis–Juxtavascular nodule – 3D filter correlation method3D image processing

3D pulmonary vessel extraction3D morphological matching algorithm

1. Kim JS, Kim JH, Cho G, Bae KT. Automated detection of pulmonary nodules on CT images: effect of section thickness and reconstruction interval--initial results. Radiology 2005; 236:295-299.

2. Bae KT, Kim JS, Na YH, Kim KG, Kim JH. Pulmonary nodules: automated detection on CT images with morphologic matching algorithm--preliminary results. Radiology 2005; 236:286-293.

1. Introduction2. Materials &

Methods3. Results4. Conclusion


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Method: Proposed Algorithm (3 type Lung Nodule)

Isolated nodule Pleural nodule Juxtavascular nodule

3D morphological matching method

Apply 3D shape & geometric determinants




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Method: Proposed Algorithm (Overall Diagram)

CT Image

Segmentation of thorax & lung

Refine lung boundary

Segmentation of lung structures

Generation of 3D lung data

3D lung data

Non-vessel group containing

isolated, pleural nodule

Vessel containing juxtavascular

nodule

3DMM to segment juxtavascular nodule

Apply shape & geometric determinants

Nodule




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Method: Proposed Algorithm (Segmentation)

Segmentation process from original CT image to binary image. With morphological filter and image processing algorithm, organs inside lung boundary were extracted. After this process 3D image processing algorithm will be performed. After 2D Segmentation process, all binary images were stacked to generate a 3D volumetric data which is shown right side.




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Method: Proposed Algorithm (Vessel grouping)

3D region growing, labeling

Non-vessel group: (Isolated, Pleural)

CT images

Segmentation of 2D lung region

Vessel group: (Juxtavascular)




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Method: 3DMM Algorithm

3D shape features in non-vessel group detection

Volume

Compactness

Elongation factor

3D Morphological Matching method in vessel group

3D morphological filter– spherical in shape, 4 sizes (3,6,9 and 12 mm in diameter)

The correlation between 3D data (I) and 3D shape filter (F)

Threshold values–Threshold value was determined empirically at 70%

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Method: MATLAB Coding

Main GUI programOpen filesDICOM viewer2D segmentation3D volume generation3D labelingShape analysis3DMM process3D visualizationJPG exportEtc…




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Total 13 nodules100% sensitivity2 false positives20 min processing time

Results: 3D visualization




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Results

Subject20 patients (mean age 57 years), mean 265 images per patientMulti-slice CT (Volume Zoom), 4x1 collimation, 0.5s scan, 120 KvP, 120 mAs1 mm slice thickness, 1mm reconstruction intervals

Total 164 nodules (20 patients) 18 patients: 1-13 nodules (mean 4.7)2 patients: 25, 54 nodules # of nodules by size (diameter): 27 (>10mm), 80 (5-10mm), and 57 (3-5mm)

size locationTotal

Diameter 3-5mm 5-10mm >10mm Isolated pleural vascular

Total nodules 57 80 27 78 52 34 164

CAD results 52 79 25 76 48 32 156

Sensitivity 91.2% 98.8% 92.6% 97.4 92.3 94.1 95.12%

Bae KT, Kim JS, Na YH, Kim KG, Kim JH. Pulmonary nodules: automated detection on CT images with morphologic matching algorithm--preliminary results. Radiology 2005; 236:286-293




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Conclusion

Performance of CAD Algorithm 20 patients, 164 solid pulmonary nodule High sensitivity: average 95.12%

– Specially 94.1% for juxtavascular SPN Low false positive rate: 4.0 per one patient Modular design of 3D algorithm for types in terms of their proximity to

surrounding anatomic structure shows excellent performance.

Verification of CAD algorithm Comparisons with other algorithm using common data is not completed.

– Lung image database consortium (LIDC, NIH) : CT image acquisition are not taken in the whole lung region. CT Images of LIDC database are inadequate to apply our algorithm. No CAD research is done with LIDC database




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Contents

1. Introduction



4. Ground Glass Opacity Detection Algorithm1. Introduction2. Materials & Method

1. Subjects2. GGO CAD Algorithm

3. Results4. Conclusion

5. Conclusion


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Introduction: What is Ground Glass Opacity (GGO)?

I-ELCAP Definitiona CT finding of a partially-opaque region that does not obscure the structures contained within

Manifestations of lung cancer on CTSolid nodule: most common Nodule with GGO (part-solid nodules or nonsolid): 20%

–higher malignancy likelihood

Localized GGOMay not detected with most CAD systemMeasurement of GGO’s volume is important for diagnosis




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Introduction: Previous GGO CAD algorithm

General 2D slice CT image & Texture only with ROI Extraction GGO from complicated structures. Classification tool: Neural Networks (MLP)

1. Kim KG, Goo JM, Kim JH, Lee HJ, Min BG, Bae KT, Im JG.Computer-aided diagnosis of local ized ground-glass opaci ty in the lung at CT: ini t ial experience. Radiology. 2005 Nov;237(2):657-61.

2. Uchiyama Y, Katsuragawa S, Abe H, Shiraishi J, Li F, Li Q, Zhang CT, Suzuki K, Doi K. Quantitat ive computerized analysis of dif fuse lung disease in high-resolut ion computed tomography . Med Phys. 2003 Sep;30(9):2440

3. Kauczor HU, Heitmann K, Heussel CP, Marwede D, Uthmann T, etc Automatic detect ion and quant i f icat ion of ground-glass opacit ies on high-resolut ion CT using mult iple neural networks: comparison wi th a dens ity mask. Am J Roentgenol. 2000 Nov;175(5):1329-34.

4. International Conferences (SPIE, RSNA, CARS) posters and papers.




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Introduction: Practical Problems Solution

Segmentation of GGOVolume is very important parameter for lung cancer follow-up. 2D ROI based CAD can not segment the exact area and calculate the volume of GGO.

Exact segmentation and volume measurement of GGO using 3D image processing

Texture Parameter, ANN DependencyOnly gray value (HU) informationKurtosis, surface curvature, inertia, momentum, Entropy, energy, skewness, mean etc…

6~96 features was used for analysis.2~3 layer multi-layer perceptron, kNN training, multiple neural network were used as classification tool.

Classification GGO with other parameters (volume, shape, simple texture) without ANN.

High False Positive rateHigh false positive rate due to texture analysis and ANN

Classification GGO with other parameters (volume, shape, simple texture) without ANN.

Algorithm for only pure GGOSingle algorithm for pure GGOSPN algorithm + GGO algorithm = detection of mixed GGO.

2005 RadiologyKim KG et al.

2006 Med PhysSluimer et al.




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Introduction: Motivation

Practical ProblemSegmentation of exact area or volume of GGOInput parameter dependency

–General 2D slice CT image & Texture only with ROI, Neural Networks (MLP)High false positive rate Algorithm for only pure GGO

3D GGO CAD Algorithm3D image processing (segmentation, region growing, visualization) using MDCT

–3D cross mask processingRule based classification toolMixed GGO detection changing threshold value



The Purpose is… The Purpose is… To develop an automatic CAD algorithm To develop an automatic CAD algorithm

for Ground-Glass Opacity with Mult i-Detector CTfor Ground-Glass Opacity with Mult i-Detector CT

3. Kim JS, J.W Lee, J.H Kim, G. Cho, J.M Goo, Computer Aided Detection of Ground Glass Opacity using Multi-Detector CT, (in preparation for submission to Korean Journal of Radiology)

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Method: Subject

Patients data17 case (Male : 7, Female :10, mean age: 56)

– reviewed by two chest radiologists Average 305 images/case (total 5186 images)MDCT Image

–Seoul National University Hospital, ChungNam National University Hospital–Sensation 16, Siemens, LightSpeed Ultra, GE, Mx8000, Philips–120KVp, 120 effective mAs, 0.5s scan time–Reconstruction interval, slice thickness: 1.00 ~ 1.25 mm–512x512 16bits DICOM image

Ground Glass Opacity of DataSize (mm) 3 ~ 5 6 ~ 10 11 ~ Total

Mixed GGO 1 2 9 12

Pure GGO 12 9 11 32

Total # of GGO 13 11 20 44




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Method: CT image including GGO




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Method: Concept of GGO CAD

Flow diagram illustrating the overall scheme for automated GGO detection from CT images.

Overall idea for GGO detection using MDCT images. The main components in lung CT image of an abnormal patient who has GGO were air, soft tissue, GGO and some noises.




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Method: Decision of Threshold Value

-1024 -800 -700 -500 -400 -200

Initial range of GGO threshold value

Optimal thereshold value of GGO

Current standard of threshold value of SPN

-800 HU ~ -200 HU -700 HU ~ -400 HU -500HU ~




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Method: Segmentation

Segmentation process from original CT image to binary image. With morphological filter and image processing algorithm, organs inside lung boundary were extracted. After this process 3D image processing algorithm will be performed. After 2D Segmentation process, all binary images were stacked to generate a 3D volumetric data which is shown right side.




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Method: 3D Cross Mask

GGO candidate Extraction from 3D data set3D Cross mask technique

–3D morphological filtering process

Ground glass opacity–Defining GGO on the basis of shape isdifficult and subjective.–But, they have fuzzy and star like shape.

We decide the optimal distance between center and next GGO candidate voxel as 3 pixels (1.5~2mm)

3D morphological process illustrating the 3D cross mask. 3D morphological filtering process with 3D cross mask initiates at the peaks in the marker volume and spreads throughout the rest of the volume based on the connectivity of the voxels.




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Method: Application of 3D Cross Mask

Before 3D cross mask processThreshold value

: -700 HU ~ -400 HU

After 3D cross mask processThreshold value

: -700 HU ~ -400 HU




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Method: Criteria after 3D Cross Mask

Classification criteria for GGO

Elongation factor

Volume–GGO has some volume in 3 dimension.–GGO larger than 3cm is meaningless.

Peak to Edge density ratio

3D profile

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Results: Overall Performance

GGO CAD Sensitivity

Processing TimePentium IV 3.0 GHz, RAM2.0 GByteMicrosoft Windows XP, Processing time : (average) 110 seconds

False Positive0.5 GGO/ case

Size (mm) 3 ~ 5 6 ~ 10 11 ~ Total

Mixed GGO100%(1/1)

100%(2/2)

100%(9/9)

100%(12/12)

Pure GGO50%

(6/12)88.9%(8/9)

90.9%(10/11)

75%(24/32)

Total # of GGO53.8%(7/13)

90.9%(10/11)

95%(19/20)

81.9%(36/44)




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Results: CAD foundings (Pure GGO)

CAD found 6 mm size pure GGO




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Results: Visualization GGO in volume



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CAD found mixed GGO, 16 mm

Results: CAD foundings (Mixed GGO)




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Discussion & Conclusion

Ground-Glass Opacity can be detected from multi-slice CT

by the 3D cross mask and shape analysis algorithm

with high sensitivity and a relatively low false positives.

Segmentation and volume measurement of GGO is performed. High performance without artificial neural network including detection of mixed GGO. Our GGO CAD algorithm is focused on the low false positive rate (< 1 fp/case) than high sensitivity. If we allow 4 fp/case, we can detect small GGOs (3~5mm) without problems.

Most of false negatives are less than 5mm. –GGO smaller than 5mm is not important for lung disease.

With a large scale study, the performance, specially statistical analysis and clinical application of the proposed GGO detection algorithm should be examined.




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Contents

1. Introduction




5. Conclusion1. Summary2. Conclusion


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Summary for Lung Nodule CAD

Research Group Sensitivity False positive Contents

Armato SG(Med.Phys 2001)

70%(89%)

1.5 /image(1.3 /image)

9 features(2D, 3D)First researth using 2.5 D

Linear discriminant analysis

Jane Ko(Radiology 2001)

86% unspecified

location, shape, volumebased on time study


Brown MS(Radiology 2002)

100%70%

15 /case3D shape information


Reeves AP(SPIE 2004)

95.7% 19.3 / caseOnly pleural nodule

3D search space, 3D connectionNot applied juxtavascular nodule

Lee JW(Invest Radiol 2004)

81% 28.8 / case

3D recursive analysisUse of Radial distribution

Performance dependency on locationDetection error large SPN

Jin Sung Kim(Radiology 2005)

95.12% 4 / case

3D morphological matching algorithmHigh sensitivity on juxtavascular nodule, Low false positive rate using modular algorithm on their type

Detection Error with Large SPNIntroduction Objective & Scope SPN Detection ConclusionGGO Detection

Practical Problems wereHigh detection error with vessel componentHigh false positive rateDetection error with large solid pulmonary nodule

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Summary for GGO CAD

Research Group

Methods Results

Kim KGRadiology. 2005

2D CT image processing 30 x 30 ROI (50% overlap)

2D texture feature2 layer ANN

With a threshold of 0.9 (ANN)Sensitivity: 94.3% (280/297 ROIs)

0.89 false positive /imageDisplay the square ROI box

Uchiyama YMed Phys 2003

For diffuse lung disease32 x 32 (96 x 96) ROI analysis

12 feature, 3 layer ANN

Abnormal caseSensitivity: 99.2% (122/123 ROIs)Didn’t mention false positive rate Not exact volume (only existence)

Kauczor HU, HeitmannAJR 2000,

Eur Radiol 1997

Not given detailsROI based texture analysis

Multiple neural network

6% classified as GGO of total2nd classified 99% of GGO

False positive: 24%

Jin Sung KimKJR 2006 preparing

3D ROI Selection3D morphological Analysis

3D cross maskRule-based Analysis

Exact GGO segmentationVolume information of GGO

Sensitivity: 81.9 %, 0.5 fp / caseDetection of mixed, pure GGO


Practical Problems wereSegmentation of exact area or volume of GGOInput parameter dependency, High false positive rate,Algorithm for only pure GGO

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Conclusion

New automatic computer aided detection (CAD) method was developed

for lung disease using multi-detector CT images. Solid Pulmonary Nodule (SPN) Detection

–We developed an automated CAD program (3D morphological matching method) that takes advantage of thin-section 3D volumetric data of MDCT images for pulmonary nodules based on 3 different types (isolated, juxtapleural, juxtavascular). The results of our study demonstrated that a CAD system could detect nodules with high sensitivity and a relatively low false-positive detection rate.

Ground Glass Opacity (GGO) Detection–We developed a 3D CAD algorithm for localized GGO detection that apply ‘3D cross mask’ using MDCT images without artificial neural network. The performance, such as exact GGO segmentation, sensitivity and false positive rate of GGO CAD algorithm is superior to previous studies.

Our developed CAD system may assist radiologists in the interpretation

of CT images, particularly for lung cancer screening.


감사합니다

presentation for ph.d in 2006

Education