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PhD defense presentation, February 2007. Methods for computer aided detection of pulmonary embolism and arterial tree segmentation.
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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
Introduction • Segmentation • Detection • Results • Conclusion
Context
4
Introduction • Segmentation • Detection • Results • Conclusion
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
Introduction • Segmentation • Detection • Results • Conclusion
Truncular
Left Right
lobarlobarsegmental segmental
subsegmental subsegmental
1 2 3 4 52345
Blood Clots
7
Introduction • Segmentation • Detection • Results • Conclusion
ClotsClots
• Computer Aid to Radiologists‣ Preliminary detection step‣ Increase time-efficiency of exams
• Remark‣ Does not decide for the expert
Project Goals
8
Introduction • Segmentation • Detection • Results • Conclusion
• New problem, few solutions
• Masutani et al. 2000-2003‣ Segmentation + detection‣ Region growing algorithm
• Liang et al. 2005‣ Tobogganning
State of the Art
9
Introduction • Segmentation • Detection • Results • Conclusion
• 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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Introduction • Segmentation • Detection • Results • Conclusion
• 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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Introduction • Segmentation • Detection • Results • Conclusion
• Zahlten, Bruijns‣ Define a “wave”
• Features‣ Allow the construction of the vessel-tree‣ Voxel-based
‣ Overdetection of bifurcations
Existing Methods
12
Introduction • Segmentation • Detection • Results • Conclusion
(a) (b) (c) (d)
• Deschamps uses Fast Marching‣ Interface propagation with freezing
Existing Methods
13
Introduction • Segmentation • Detection • Results • Conclusion
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
15
Introduction • Segmentation • Detection • Results • Conclusion
• 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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Introduction • Segmentation • Detection • Results • Conclusion
17
• Touching vessels
Extension
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Arteries Arteries
Arteries
Aorta AortaAorta
Vena
cava
Vena
cava
Vena
cava
Veins Veins
Introduction • Segmentation • Detection • Results • Conclusion
• 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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Introduction • Segmentation • Detection • Results • Conclusion
3-D Parametric Model
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Segment i
Introduction • Segmentation • Detection • Results • Conclusion
21
• 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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Introduction • Segmentation • Detection • Results • Conclusion
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
24
vessel 2
vessel 1
P
d
Introduction • Segmentation • Detection • Results • Conclusion
25
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
26
Introduction • Segmentation • Detection • Results • Conclusion
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
27
Introduction • Segmentation • Detection • Results • Conclusion
• 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
Introduction • Segmentation • Detection • Results • Conclusion
• More sensitive than M1
• A lot of false positives
• Necessity of extracting lung interior volume
M2 Detection
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Introduction • Segmentation • Detection • Results • Conclusion
30
• Comparison
• Combining with logical OR
Combining Methods
31
M1 M2
Introduction • Segmentation • Detection • Results • Conclusion
• 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
32
Introduction • Segmentation • Detection • Results • Conclusion
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
35
Introduction • Segmentation • Detection • Results • Conclusion
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
37
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.
Introduction • Segmentation • Detection • Results • Conclusion
Results
38
Sensitivity Specificity ROI
Mean 88% 99% [0.85%, 2.34%]
Std. Dev. 10% 0.39% —
• Sensitivity generally high
• Misleading specificity
Introduction • Segmentation • Detection • Results • Conclusion
39
• Segmentation OK, until N bifurcations
• Many false alarms
• Non detection are generally non critical
‣ Hypothesis not valid for certain clots
• Répartition des détections
Analysis
40
Sensibilité
M1 entre 0 et 51%
M2 entre 57 et 100%
Introduction • Segmentation • Detection • Results • Conclusion
• Proof of concept
• There is still room to optimize
Computing time
41
Algorithm CPU Time
Arteries Segmentation 35 sec.
Lung Segmentation 12 sec.
M1 Detection 7 min.
M2 Detection 6 min. 45 sec.
Introduction • Segmentation • Detection • Results • Conclusion
• Presentation of a complete framework
• Slice Marching introduction + model extension
• Implementation of 2 detection methods
• Presentation of a validation scheme
Conclusion
42
Introduction • Segmentation • Detection • Results • Conclusion
• 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
43
Introduction • Segmentation • Detection • Results • Conclusion
• 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
44
Introduction • Segmentation • Detection • Results • Conclusion
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