Computer-aided Diagnosis of Pulmonary Embolism in Opacified CT Images

Computer-aided Diagnosis of Pulmonary Embolism in Opacified CT Images

Ph.D. Defense Presentation by Raphaël Sebbe

TCTS Lab — Faculté Polytechnique de Mons LESI Lab — Polytech’Orléans

Plan

• Context Introduction

• Methods‣ Segmentation of arteries‣ Blood Clot detection‣ Assessment of performances

• Results

• Conclusion

2

Introduction • Segmentation • Detection • Results • Conclusion

Context

• Pulmonary Embolism ‣ Definition‣ Symptoms

‣ Diagnosis

• Medical Imaging‣ CT Scanner

‣ Computer Aid ?

3


Context

4


Det

ecto

r

X-ray

source

5

Axial

SagitalCoronal

Combined

• Image Acquisition Protocol‣ Contrast product is used‣ A blood clot is a dark spot

• Different clot types‣ Acute vs. chronic ⇔ Central vs. Peripheral

Blood Clots

6


Truncular

Left Right

lobarlobarsegmental segmental

subsegmental subsegmental

1 2 3 4 52345

Blood Clots

7


ClotsClots

• Computer Aid to Radiologists‣ Preliminary detection step‣ Increase time-efficiency of exams

• Remark‣ Does not decide for the expert

Project Goals

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• New problem, few solutions

• Masutani et al. 2000-2003‣ Segmentation + detection‣ Region growing algorithm

• Liang et al. 2005‣ Tobogganning

State of the Art

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• Methods‣ Segmentation pulmonary arteries‣ Analysis/detection of blood clots

‣ Evaluation of performances

• Constrains‣ Limited computing time‣ Usability, presentation of results

Approach

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• State of the Art‣ Filtering methods‣ Region growing methods

‣ Model-based methods

• Why use region growing methods?‣ Elongated shapes (vessels)‣ Opacification means high contrast

‣ Multiscale structures (fractal-like)‣ 3-D

Segmentation

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• Zahlten, Bruijns‣ Define a “wave”

• Features‣ Allow the construction of the vessel-tree‣ Voxel-based

‣ Overdetection of bifurcations

Existing Methods

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(a) (b) (c) (d)

• Deschamps uses Fast Marching‣ Interface propagation with freezing

Existing Methods

13


Fast Marching

• Mathematical expression complex

• Easy interpretation

• Lifeguard Example

14

☹☺

• We proposed the Slice Marching algorithm‣ Fast Marching -based, originally introduced by Sethian‣ Speed of propagation F is defined at each voxel location

‣ Slices of vessels are defined‣ The vessel-tree is rebuilt

Slice Marching

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• Novelties of Slice Marching‣ Cuts the vessel in variable-depth slices:

generalizes the work made by Zahlten and Bruijns

‣ Introduction of a speed of propagation: enables the use of higher-level information

‣ Allows vessel features computation: section, curvature, etc.

Slice Marching

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• Touching vessels

Extension

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Arteries Arteries

Arteries

Aorta AortaAorta

Vena

cava

Vena

cava

Vena

cava

Veins Veins


• Novel Solution Based on:‣ Adding a model knowledge‣ Modifying the speed of propagation

• Model is a Set of 3-D Parametric Curves‣ Defined on one patient,‣ Co-registered (adapted) to other patients,

‣ Used to modify the speed of propagation of wavefront in vessels.

Extension

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3-D Parametric Model

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Segment i


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• Fiducial points are used

• Manual localizing of these points is required

• Non linear transformation of space with Thin-Plates splines

Co-registration

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23

Placement des points fiduciels Création du modèle

Placement des points fiduciels Déformation du modèle

Patient A

Patient B

• The model modifies the speed

• Intrinsically handle by fast marching

Model Use

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vessel 2

vessel 1

P

d


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No Model is Used Use of a Model

• First Approach (M1)‣ Detection of opacification defects that create a

concavity in extracted vessels

‣ 3-D image processing operators are used to reveal these concavities

Blood Clot Detection

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PA

Segmentation

Morphology

ClosingXOR

Threshold

Grey

AND

CT

• Good/bad results‣ Peforms well for clots in the larger sections of

PA

‣ Performance is decreased for peripheral clots

• Depends on quality of segmentation

• Another method must be developed

M1 Detection

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• Think again: what is a clot?‣ Vessel independence ‣

M2 Detection

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Threshold

Grey

Plane XY

Selection

Plane YZ

Selection

Plane ZX

Selection

ORCTMorphology

Opening


• More sensitive than M1

• A lot of false positives

• Necessity of extracting lung interior volume

M2 Detection

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• Comparison

• Combining with logical OR

Combining Methods

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M1 M2


• Masutani et al. localize clots and then count them

• Liang et al. do not tell about their evaluation scheme

• No standard scheme was found

• Our thinking is:‣ A clot is not a point‣ If you say so, you need a threshold distance

Validation

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33

34

• Our approach: a 3-D grid is used.‣ grid boxes with a clot inside are marked‣ this is the ground truth.

• Size of the 3-D grid‣ Small boxes = accuracy in clot localization

‣ Big boxes = ease of creating the GT

• Time needed by radiologist: 30min @ 16 vox.

Validation

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36

• Quantitative measure of performance: ‣ Detection -> true/false, positive/negative

‣ Sensitivity

‣ 100% = all clots are detected

‣ Specificity‣ 100% = no false detection

• Qualitative analysis with 3-D visualization‣ Express problems (discuss this with other research

domains)

Visualization and Measures

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80 Methods

➡

➡

Figure 4.24: The voxel boxes has to be highlighted even if the emboli is onlypartially contained. The difficult cases are marked by an arrow, where onlya few voxels have lower densities and can easily be missed. The third imageshows a case where a small vessel with a clot is across two boxes.

4.6.2 Measuring Sensitivity and Specificity

In traditional binary classification, false positives (FP) are defined as thecases where the detection method detects a clot while the expert does not.A false negative (FN) is the dual situation where experts detect a clot andthe method does not. True positives (TP) and true negatives (TN) are forthe cases when both agree, respectively detecting a clot (positive) and notdetecting a clot (negative). Sensitivity (Sn) and specificity (Sp) are definedby the following formulas:

Sn =NTP

NTP + NFN, Sp =

NTN

NTN + NFP(4.28)

where NTP , NTN , NFP , and NFN respectively are the number of occurrencesof true positive, true negative, false positive, and false negative classes.

We note that both concepts do not have the same importance dependingon the context in which they are used. In the medical diagnosis context,non-detections should generally be avoided as much as possible, as this candirectly lead to health related consequences and a bad specificity may impactthe value of a CAD tool.

Another remark is that the sensitivity and specificity concepts are gen-erally used patient-wise in medicine. This is not the case here and it shouldnot be confused, as these concepts apply here to the boxes of voxels. Theyqualify the capacity of the method to detect emboli for each box of voxelsand not for each patient.

These values can be computed when both the ground truth and detectionresults are available, and even used to compare various parameters of thesame method. A qualitative method based on visualization is also possibleby color coding the boxes of voxels depending on the correspondence of themethod and the ground truth. This has been implemented and is shown inFigure 4.25.

80 Methods

➡

➡

Figure 4.24: The voxel boxes has to be highlighted even if the emboli is onlypartially contained. The difficult cases are marked by an arrow, where onlya few voxels have lower densities and can easily be missed. The third imageshows a case where a small vessel with a clot is across two boxes.

4.6.2 Measuring Sensitivity and Specificity

In traditional binary classification, false positives (FP) are defined as thecases where the detection method detects a clot while the expert does not.A false negative (FN) is the dual situation where experts detect a clot andthe method does not. True positives (TP) and true negatives (TN) are forthe cases when both agree, respectively detecting a clot (positive) and notdetecting a clot (negative). Sensitivity (Sn) and specificity (Sp) are definedby the following formulas:

Sn =NTP

NTP + NFN, Sp =

NTN

NTN + NFP(4.28)

where NTP , NTN , NFP , and NFN respectively are the number of occurrencesof true positive, true negative, false positive, and false negative classes.

We note that both concepts do not have the same importance dependingon the context in which they are used. In the medical diagnosis context,non-detections should generally be avoided as much as possible, as this candirectly lead to health related consequences and a bad specificity may impactthe value of a CAD tool.

Another remark is that the sensitivity and specificity concepts are gen-erally used patient-wise in medicine. This is not the case here and it shouldnot be confused, as these concepts apply here to the boxes of voxels. Theyqualify the capacity of the method to detect emboli for each box of voxelsand not for each patient.

These values can be computed when both the ground truth and detectionresults are available, and even used to compare various parameters of thesame method. A qualitative method based on visualization is also possibleby color coding the boxes of voxels depending on the correspondence of themethod and the ground truth. This has been implemented and is shown inFigure 4.25.


Results

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Sensitivity Specificity ROI

Mean 88% 99% [0.85%, 2.34%]

Std. Dev. 10% 0.39% —

• Sensitivity generally high

• Misleading specificity


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• Segmentation OK, until N bifurcations

• Many false alarms

• Non detection are generally non critical

‣ Hypothesis not valid for certain clots

• Répartition des détections

Analysis

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Sensibilité

M1 entre 0 et 51%

M2 entre 57 et 100%


• Proof of concept

• There is still room to optimize

Computing time

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Algorithm CPU Time

Arteries Segmentation 35 sec.

Lung Segmentation 12 sec.

M1 Detection 7 min.

M2 Detection 6 min. 45 sec.


• Presentation of a complete framework

• Slice Marching introduction + model extension

• Implementation of 2 detection methods

• Presentation of a validation scheme

Conclusion

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• Positionnement

• Echanges avec le milieu médical

• Applications possibles‣ CAD pour l’embolie pulmonaire‣ Outil d’analyse des vaisseaux

‣ Mesure automatique des scores d’embolie

• Perspectives‣ Classification pour affiner la détection

Conclusion

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• Publications‣ Participations à 7 conférences nationales (2) et

internationales (5)

• Prix‣ Premier prix colloque Sciences en Sologne‣ Prix Sadron du Rotary Val de Loire

Conclusion

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Technology

Computer-aided Diagnosis of Pulmonary Embolism in Opacified CT Images