HCC (hepatocellular carcinoma) is the most common primary liver cancer, and the third leading cause of death worldwide. Diagnosis is generally conducted through various medical imaging modalities (ultra-sound, CT, MRI) and, depending on the characterization of HCC (number and size of nodules, early or later staging, etc.), different therapeutic strategies can be delivered to the patient: radiofrequency ablation, liver resection surgery, chemo-embolization, etc. During this talk, I first present a novel ontological approach to represent both HCC detection, staging and treatment into a single information system framework, enabling a complete digital patient follow-up. Since representing numerically liver's geometry is an important concern in such system, I then expose our most recent algorithms devoted to reconstruct the liver volume and inner vessels in 3-D from CT and MRI data. We also see different applications employing the outcomes provided by these tools. (1) The standardized Couinaud liver representation can be calculated thanks to the shape of the vasculature, and permits to locate HCC nodules in a reproducible way. (2) By isolating liver volume, we have proposed to automatically detect HCC tissues within DCE-MRI (dynamic contrast-enhanced MRI) sequences by two approaches: SVM-based classification and adapted U-Net deep learning. (3) We have also studied the numerical simulation of hepatic perfusion by considering finite-element models of the liver and its vessels. This talk finishes by exposing our future prospects in improving our methodologies and combining them for proposing novel computer-aided HCC diagnosis and treatment systems.

1. IMAGE-GUIDED LIVER CANCER MODELING FOR
COMPUTER-AIDED DIAGNOSIS
AND TREATMENT
CIM COSY SEMINAR 2018
ANTOINE VACAVANT, PHD, HDR, ASSOCIATE PROFESSOR
INSTITUT PASCAL, UMR6602 UCA / SIGMA / CNRS, LE PUY-EN-VELAY
www.linkedin.com/in/antoinevacavant
twitter.com/antoinevacavant
antoine.vacavant@uca.fr
antoine-vacavant.eu

2. Outline
1. Me in one slide
2. Context and motivation
3. Ontology-based liver cancer diagnosis and treatment
4. Liver segmentation
5. Hepatic vascular network segmentation
6. Couinaud representation
7. HCC detection by machine learning approaches
8. Numerical simulation of hepatic blood flow
9. Conclusion and future works
Antoine Vacavant 1 / 39

3. Outline
1. Me in one slide
2. Context and motivation
3. Ontology-based liver cancer diagnosis and treatment
4. Liver segmentation
5. Hepatic vascular network segmentation
6. Couinaud representation
7. HCC detection by machine learning approaches
8. Numerical simulation of hepatic blood flow
9. Conclusion and future works
Antoine Vacavant 2 / 39

4. Me in one slide /
Université Clermont Auvergne
2010 - now: Associate prof. in computer science,
Institut Pascal, IUT Le Puy-en-Velay
Researches in IGT / Image Guided Therapies
2010 - 2015: Head of bachelor degree in computer graphics
2017 - now: Responsible of tech transfer in IGT
2017 - now: Scientific head of Embolization research team
Computer
vision
Image
processing
Spatial
data
structures
Digital
geometry
Medical
appli-
cations
Antoine Vacavant 3 / 39

5. Outline
1. Me in one slide
2. Context and motivation
3. Ontology-based liver cancer diagnosis and treatment
4. Liver segmentation
5. Hepatic vascular network segmentation
6. Couinaud representation
7. HCC detection by machine learning approaches
8. Numerical simulation of hepatic blood flow
9. Conclusion and future works
Antoine Vacavant 4 / 39

6. Context and motivation
Our research group
Inside IGT research axis
CaVITI (Cardio-Vascular Interventional Therapy and Imaging)
3 research groups
Theme 1: Endoprothesis
Theme 2: Embolization
Theme 3: Myocardial function
Antoine Vacavant 5 / 39

7. Context and motivation
Our research group
Inside IGT research axis
CaVITI (Cardio-Vascular Interventional Therapy and Imaging)
3 research groups
Theme 1: Endoprothesis
Theme 2: Embolization
Theme 3: Myocardial function
Research targets
Quantitatively assess hepatic tumoral response by medical image analysis
Innovative tools devoted to tumoral tissue quantification
Personalized numerical simulation of treatments
Link with clinical activities: chemo-embolization, surgery, biopsy, etc.
Target cancer: HCC (Hepato-Cellular Carcinoma)
Antoine Vacavant 5 / 39

8. Context and motivation
The liver
Its vascular system:
2 blood inflows (portal vein + hepatic artery)
1 blood outflow (hepatic vein)
Subdivided into complex tree-like networks
Each hepatocyte connects to those networks,
with many functions
Synthesize proteins
Secrete bile
Detoxify, etc.
Antoine Vacavant 6 / 39

9. Context and motivation
The liver
Its vascular system:
2 blood inflows (portal vein + hepatic artery)
1 blood outflow (hepatic vein)
Subdivided into complex tree-like networks
Each hepatocyte connects to those networks,
with many functions
Synthesize proteins
Secrete bile
Detoxify, etc.
Couinaud’s conventional representation
Standardized segmentation of the liver
Depending on hepatic vasculatures
Application: localization of liver tumors
Antoine Vacavant 6 / 39

10. Context and motivation /
HCC / Hepato-Cellular Carcinoma
500,000+ new cases / year in the World
5th cause of cancer in the World
3rd cause of death by cancer
Uni- or multi-focal hypervascularized nodules
Causes of HCC
Appear with cirrhosis at a rate of 80%
Hepatitis B/C, alcohol intoxication,
diabetes, etc.
(a) (b)
(c) (d)
Antoine Vacavant 7 / 39

11. Context and motivation /
HCC / Hepato-Cellular Carcinoma
500,000+ new cases / year in the World
5th cause of cancer in the World
3rd cause of death by cancer
Uni- or multi-focal hypervascularized nodules
Causes of HCC
Appear with cirrhosis at a rate of 80%
Hepatitis B/C, alcohol intoxication,
diabetes, etc.
(a) (b)
(c) (d)
HCC radiological diagnosis
Several possible internal imaging observations (US, CT, MRI)
Better diagnosis with contrast-enhanced MRI (DCE-MRI)
Validation by biopsy
Antoine Vacavant 7 / 39

12. Outline
1. Me in one slide
2. Context and motivation
3. Ontology-based liver cancer diagnosis and treatment
4. Liver segmentation
5. Hepatic vascular network segmentation
6. Couinaud representation
7. HCC detection by machine learning approaches
8. Numerical simulation of hepatic blood flow
9. Conclusion and future works
Antoine Vacavant 8 / 39

13. Ontology-based liver cancer diagnosis and treatment
Ontologies and information systems
To enable more automatic decisions upon HCC diagnosis and treatment
In a computer-aided approach
Facing the dramatical increase of medical data
R. Messaoudi, F. Jaziri, A. Mtibaa, M. Grand-Brochier, H. Mohamed Ali, A. Amouri, H. Fourati, P. Chabrot, F. Gargouri, A. Vacavant: Ontology-based
Approach for Liver Cancer Diagnosis and Treatment. Journal of Digital Imaging, 2018.
Antoine Vacavant 9 / 39

14. Ontology-based liver cancer diagnosis and treatment
Ontologies and information systems
To enable more automatic decisions upon HCC diagnosis and treatment
In a computer-aided approach
Facing the dramatical increase of medical data
Ontologies are efficient to model HCC for both concerns
Contribution
Model HCC detection and characterization
Staging HCC
Following standard conventions
Implemented in a Java-based framework
R. Messaoudi, F. Jaziri, A. Mtibaa, M. Grand-Brochier, H. Mohamed Ali, A. Amouri, H. Fourati, P. Chabrot, F. Gargouri, A. Vacavant: Ontology-based
Approach for Liver Cancer Diagnosis and Treatment. Journal of Digital Imaging, 2018.
Antoine Vacavant 9 / 39

15. Ontology-based liver cancer diagnosis and treatment
Ontologies and information systems
To enable more automatic decisions upon HCC diagnosis and treatment
In a computer-aided approach
Facing the dramatical increase of medical data
Ontologies are efficient to model HCC for both concerns
Contribution
Model HCC detection and characterization
Staging HCC
Following standard conventions
Implemented in a Java-based framework
Original integrative approach wrt. state of the art
R. Messaoudi, F. Jaziri, A. Mtibaa, M. Grand-Brochier, H. Mohamed Ali, A. Amouri, H. Fourati, P. Chabrot, F. Gargouri, A. Vacavant: Ontology-based
Approach for Liver Cancer Diagnosis and Treatment. Journal of Digital Imaging, 2018.
Antoine Vacavant 9 / 39

16. Ontology-based liver cancer diagnosis and treatment
Conventions practiced in clinical routine
LI-RADS (Liver Imaging Reporting and Data System)
Antoine Vacavant 10 / 39

17. Ontology-based liver cancer diagnosis and treatment
Conventions practiced in clinical routine
LI-RADS (Liver Imaging Reporting and Data System)
BCLC (Barcelona Clinic Liver Cancer)
Antoine Vacavant 10 / 39

18. Ontology-based liver cancer diagnosis and treatment
Conventions practiced in clinical routine
LI-RADS (Liver Imaging Reporting and Data System)
BCLC (Barcelona Clinic Liver Cancer)
TNM (Tumor, Node, and Metastasis)
Antoine Vacavant 10 / 39

19. Ontology-based liver cancer diagnosis and treatment
Our approach: OntHCC
Extract information from medical data
Medical image content annotation Weasis tool for extracting patients’
data
Antoine Vacavant 11 / 39

20. Ontology-based liver cancer diagnosis and treatment
Our approach: OntHCC
Extract information from medical data
Ontology representation
Concepts &
definitions
Graph representation following SWRL
Antoine Vacavant 11 / 39

21. Ontology-based liver cancer diagnosis and treatment
Our approach: OntHCC
Extract information from medical data
Ontology representation
Software engineering
HCC detection, stadification and treatment decision
following user’s parameters
Antoine Vacavant 11 / 39

22. Ontology-based liver cancer diagnosis and treatment
Experimental evaluation
Dataset: 28 medical reports of patients suffering from HCC
Groups of users manipulate 4 different ontologies
OntHCC
ONLIRA (CaReRa project) [Kokciyan et al., 2014]
Ontology by [Alfonse et al., 2012]
LiCO [Yunzhi et al., 2016]
1 patient processed = 1 instance of the ontology
We can determine then false/true positive/negative over concepts that
are correctly covered (or not)
Antoine Vacavant 12 / 39

23. Ontology-based liver cancer diagnosis and treatment
Experimental evaluation
Dataset: 28 medical reports of patients suffering from HCC
Groups of users manipulate 4 different ontologies
OntHCC
ONLIRA (CaReRa project) [Kokciyan et al., 2014]
Ontology by [Alfonse et al., 2012]
LiCO [Yunzhi et al., 2016]
1 patient processed = 1 instance of the ontology
We can determine then false/true positive/negative over concepts that
are correctly covered (or not)
Name or ref. Recall (%) Precision (%) F-measure (%)
OntHCC 76 85 80
ONLIRA 43 69 51
[Alfonse et al., 2012] 22 62 32
LiCO 55 76 62
Antoine Vacavant 12 / 39

24. Ontology-based liver cancer diagnosis and treatment
Experimental evaluation
Dataset: 28 medical reports of patients suffering from HCC
Groups of users manipulate 4 different ontologies
OntHCC
ONLIRA (CaReRa project) [Kokciyan et al., 2014]
Ontology by [Alfonse et al., 2012]
LiCO [Yunzhi et al., 2016]
1 patient processed = 1 instance of the ontology
We can determine then false/true positive/negative over concepts that
are correctly covered (or not)
How to reach automatic image-guided assessment of HCC vascular
profile, liver segments, etc.?
Name or ref. Recall (%) Precision (%) F-measure (%)
OntHCC 76 85 80
ONLIRA 43 69 51
[Alfonse et al., 2012] 22 62 32
LiCO 55 76 62
Antoine Vacavant 12 / 39

25. Outline
1. Me in one slide
2. Context and motivation
3. Ontology-based liver cancer diagnosis and treatment
4. Liver segmentation
5. Hepatic vascular network segmentation
6. Couinaud representation
7. HCC detection by machine learning approaches
8. Numerical simulation of hepatic blood flow
9. Conclusion and future works
Antoine Vacavant 13 / 39

26. Liver segmentation
Model-based segmentation of liver volume
Automatic liver segmentation based on multi-variability representation
Four scales of statistical liver shape modeling
Applied on large datasets
CT from IRCAD and SLIVER07 datasets (88% and 93% Dice resp.)
MRI from CHU Clermont-Ferrand
CT MRI
M.-A. Lebre, K. Arrouk, A.-K. Vo Van, A. Leborgne, M. Grand-Brochier, P. Beaurepaire, A. Vacavant, B. Magnin, A. Abergel, P. Chabrot: Medical
image processing and numerical simulation for digital hepatic parenchymal blood flow. In SASHIMI@MICCAI, LNCS 10557, pages 99–108, Quebec,
Canada, 2017.
Antoine Vacavant 14 / 39

27. Outline
1. Me in one slide
2. Context and motivation
3. Ontology-based liver cancer diagnosis and treatment
4. Liver segmentation
5. Hepatic vascular network segmentation
6. Couinaud representation
7. HCC detection by machine learning approaches
8. Numerical simulation of hepatic blood flow
9. Conclusion and future works
Antoine Vacavant 15 / 39

28. Hepatic vascular network segmentation
Our pipeline for segmenting liver vessels
From a CT or MRI volume, I
Extract the liver and use it as a bounding box
Multi-scale vessel detection with Hessian matrix IS [Sato et al., 1994]
Partial skeletonization and reconnection S in IS [Homann et al., 2007]
Calculate the RORPO vesselness filter IR [Merveille et al., 2018]
Use S as initialization for fast marching segmentation within IR
M.-A. Lebre, A. Vacavant, M. Grand-Brochier, O. Merveille, A. Abergel, P. Chabrot, B. Magnin: Automatic 3-D Skeleton-based Segmentation of Liver
Vessels From MRI and CT for Couinaud Representation. In IEEE ICIP 2018, Athens, Greece,
Antoine Vacavant 16 / 39

29. Hepatic vascular network segmentation
Numerical results with IRCAD dataset (CT)
ACC SPE SEN PRE FPR FNR
Ours 0.97±0.01 0.98±0.01 0.69±0.10 0.61±0.07 0.01±0.01 0.32±0.09
RORPO 0.90±0.02 0.97±0.01 0.20±0.06 0.41±0.09 0.02±0.01 0.80±0.06
Sato 0.89±0.03 0.97±0.02 0.24±0.10 0.46±0.17 0.03±0.01 0.75±0.10
Numerical results with MRI for Couinaud representation
Sketon-based metric (first branches)
Overlap rate M0 and mean distance Md
Hepatic vein M0 (%) Md (mm)
Ours 95.46 8
RORPO 55.57 33
Portal vein M0 (%) Md (mm)
Ours 100.0 7
RORPO 72.17 33
Antoine Vacavant 17 / 39

30. Outline
1. Me in one slide
2. Context and motivation
3. Ontology-based liver cancer diagnosis and treatment
4. Liver segmentation
5. Hepatic vascular network segmentation
6. Couinaud representation
7. HCC detection by machine learning approaches
8. Numerical simulation of hepatic blood flow
9. Conclusion and future works
Antoine Vacavant 18 / 39

31. Couinaud representation
Basic principle
Vertical planes along right, middle and
left hepatic veins
Horizontal plane where the portal vein
bifurcates and becomes horizontal
Antoine Vacavant 19 / 39

32. Couinaud representation
Basic principle
Vertical planes along right, middle and
left hepatic veins
Horizontal plane where the portal vein
bifurcates and becomes horizontal
Results
Enables automatic localization of tumors
Validation with a small set of MRI volumes
Improvement: Curved planes instead of straight planes
Antoine Vacavant 19 / 39

33. Outline
1. Me in one slide
2. Context and motivation
3. Ontology-based liver cancer diagnosis and treatment
4. Liver segmentation
5. Hepatic vascular network segmentation
6. Couinaud representation
7. HCC detection by machine learning approaches
8. Numerical simulation of hepatic blood flow
9. Conclusion and future works
Antoine Vacavant 20 / 39

34. HCC detection by machine learning approaches
Computer-aided detection of HCC
HCC detection within DCE-MRI sequences
Based on parallel image processing and machine learning
Antoine Vacavant 21 / 39

35. HCC detection by machine learning approaches
Computer-aided detection of HCC
HCC detection within DCE-MRI sequences
Based on parallel image processing and machine learning
A.L.M. Pavan, M. Benabdallah, M.-A. Lebre, D.R. de Pina, F. Jaziri, A. Vacavant, A. Mtibaa, H. Mohamed Ali, M. Grand-Brochier, H. Rositi, B. Magnin,
A. Abergel, P. Chabrot: A parallel framework for HCC detection in DCE-MRI sequences with wavelet-based description and SVM classification. In
ACM ACMMIPH@SAC 2018, Pau, France, 2018.
Antoine Vacavant 21 / 39

36. HCC detection by machine learning approaches
Computer-aided detection of HCC
HCC detection within DCE-MRI sequences
Based on parallel image processing and machine learning
A.L.M. Pavan, M. Benabdallah, M.-A. Lebre, D.R. de Pina, F. Jaziri, A. Vacavant, A. Mtibaa, H. Mohamed Ali, M. Grand-Brochier, H. Rositi, B. Magnin,
A. Abergel, P. Chabrot: A parallel framework for HCC detection in DCE-MRI sequences with wavelet-based description and SVM classification. In
ACM ACMMIPH@SAC 2018, Pau, France, 2018.
Antoine Vacavant 21 / 39

37. HCC detection by machine learning approaches
Computer-aided detection of HCC
HCC detection within DCE-MRI sequences
Based on parallel image processing and machine learning
A.L.M. Pavan, M. Benabdallah, M.-A. Lebre, D.R. de Pina, F. Jaziri, A. Vacavant, A. Mtibaa, H. Mohamed Ali, M. Grand-Brochier, H. Rositi, B. Magnin,
A. Abergel, P. Chabrot: A parallel framework for HCC detection in DCE-MRI sequences with wavelet-based description and SVM classification. In
ACM ACMMIPH@SAC 2018, Pau, France, 2018.
Antoine Vacavant 21 / 39

38. HCC detection by machine learning approaches
Computer-aided detection of HCC
HCC detection within DCE-MRI sequences
Based on parallel image processing and machine learning
A.L.M. Pavan, M. Benabdallah, M.-A. Lebre, D.R. de Pina, F. Jaziri, A. Vacavant, A. Mtibaa, H. Mohamed Ali, M. Grand-Brochier, H. Rositi, B. Magnin,
A. Abergel, P. Chabrot: A parallel framework for HCC detection in DCE-MRI sequences with wavelet-based description and SVM classification. In
ACM ACMMIPH@SAC 2018, Pau, France, 2018.
Antoine Vacavant 21 / 39

39. HCC detection by machine learning approaches
Experimental evaluation
Fusion of classifications per phases into a single visualization
Radial density function considering patches
More than 80% of correct classification for 9 patients
Few false detections
Phase
Patient
#1 #2 #3 #4 #5 #6 #7 #8 #9 Global
1 0.80 0.76 0.87 0.75 0.83 0.78 0.75 0.82 0.80
2 0.98 0.95 0.81 0.93 0.96 0.77 0.87 0.77 0.83 0.86
3 0.73 0.92 0.80 0.76 0.79 0.81 0.89 0.81 0.74 0.81
Antoine Vacavant 22 / 39

40. HCC detection by machine learning approaches
Patch size matters
16×16
Phase
Patient
#1 #2 #3 #4 #5 #6 #7 #8 #9 Global
1 0.88 0.86 0.76 0.94 0.69 0.78 0.76 0.81 0.81
2 0.84 0.69 0.86 0.89 0.90 0.77 0.74 0.69 0.81 0.78
3 0.81 0.86 0.81 0.76 0.88 0.70 0.80 0.74 0.90 0.80
32×32
Phase
Patient
#1 #2 #3 #4 #5 #6 #7 #8 #9 Global
1 0.71 0.65 0.86 0.88 0.69 0.75 0.73 0.86 0.74
2 0.82 0.82 0.57 0.83 0.92 0.62 0.75 0.76 0.87 0.75
3 0.85 0.85 0.69 0.77 0.87 0.71 0.86 0.76 0.77 0.79
64×64
Phase
Patient
#1 #2 #3 #4 #5 #6 #7 #8 #9 Global
1 0.80 0.76 0.87 0.75 0.83 0.78 0.75 0.82 0.80
2 0.98 0.95 0.81 0.93 0.96 0.77 0.87 0.77 0.83 0.86
3 0.73 0.92 0.80 0.76 0.79 0.81 0.89 0.81 0.74 0.81
Antoine Vacavant 23 / 39

41. HCC detection by machine learning approaches
Parallel processing
Each image slice is decomposed into patch processed by n processors
Execution times depending on n and on dataset size
Speed-up factor of 10+ (n 16)
16×16 32×32 64×64
1 2 4 8 16
Number of processors
0
500
1000
1500
2000
2500
3000
3500
Time(sec)
9 patients
7 patients
1 2 4 8 16
Number of processors
0
100
200
300
400
500
600
Time(sec)
9 patients
7 patients
1 2 4 8 16
Number of processors
0
100
200
300
400
500
Time(sec)
9 patients
7 patients
Antoine Vacavant 24 / 39

42. HCC detection by machine learning approaches
Computer-aided detection of HCC by DL
Still a patch-based approach
U-Net needs less tranining data than conventional CNN
A. Fabijanska, A. Vacavant, M.-A. Lebre, A.L.M. Pavan, D.R. de Pina, A. Abergel, P. Chabrot, B. Magnin: U-CatcHCC: An Accurate HCC Detector in
Hepatic DCE-MRI Sequences Based on an U-Net Framework. In ICCVG 2018, Warsaw, Poland
Antoine Vacavant 25 / 39

43. HCC detection by machine learning approaches
Computer-aided detection of HCC by DL
Still a patch-based approach
U-Net needs less tranining data than conventional CNN
A. Fabijanska, A. Vacavant, M.-A. Lebre, A.L.M. Pavan, D.R. de Pina, A. Abergel, P. Chabrot, B. Magnin: U-CatcHCC: An Accurate HCC Detector in
Hepatic DCE-MRI Sequences Based on an U-Net Framework. In ICCVG 2018, Warsaw, Poland
Antoine Vacavant 25 / 39

44. HCC detection by machine learning approaches
Computer-aided detection of HCC by DL
Still a patch-based approach
U-Net needs less tranining data than conventional CNN
A. Fabijanska, A. Vacavant, M.-A. Lebre, A.L.M. Pavan, D.R. de Pina, A. Abergel, P. Chabrot, B. Magnin: U-CatcHCC: An Accurate HCC Detector in
Hepatic DCE-MRI Sequences Based on an U-Net Framework. In ICCVG 2018, Warsaw, Poland
Antoine Vacavant 25 / 39

45. HCC detection by machine learning approaches
Computer-aided detection of HCC by DL
Still a patch-based approach
U-Net needs less tranining data than conventional CNN
A. Fabijanska, A. Vacavant, M.-A. Lebre, A.L.M. Pavan, D.R. de Pina, A. Abergel, P. Chabrot, B. Magnin: U-CatcHCC: An Accurate HCC Detector in
Hepatic DCE-MRI Sequences Based on an U-Net Framework. In ICCVG 2018, Warsaw, Poland
Antoine Vacavant 25 / 39

46. HCC detection by machine learning approaches
Computer-aided detection of HCC by DL
Still a patch-based approach
U-Net needs less tranining data than conventional CNN
A. Fabijanska, A. Vacavant, M.-A. Lebre, A.L.M. Pavan, D.R. de Pina, A. Abergel, P. Chabrot, B. Magnin: U-CatcHCC: An Accurate HCC Detector in
Hepatic DCE-MRI Sequences Based on an U-Net Framework. In ICCVG 2018, Warsaw, Poland
Antoine Vacavant 25 / 39

47. HCC detection by machine learning approaches
Numerical evaluation between both approaches
Approach #1: SVM
Patch size SEN SPC PREC ACC DIC
16×16 0.733 0.800 0.250 0.794 0.372
32×32 0.800 0.756 0.301 0.764 0.437
48×48 —– —– —– —– —–
64×64 0.798 0.830 0.556 0.825 0.655
Approach #2: U-Net
Patch size SEN SPC PREC ACC DIC
16×16 0.969 0.938 0.172 0.939 0.260
32×32 0.919 0.970 0.249 0.970 0.357
48×48 0.909 0.980 0.375 0.980 0.482
64×64 0.827 0.984 0.357 0.983 0.455
Antoine Vacavant 26 / 39

48. HCC detection by machine learning approaches
Work in progress: SVM classification
Increase the size of the cohort (9 to 19 patients)
50+ MRI volumes
Still study patch size influence
Fusion of phases for tumor visualization
Improvements
MRI enhancement
Use 60+ features and select the best ones by two
approaches
Phase ACC SEN SPC TP TN FP FN #ROIs
Corrected 1 0.968 0.970 0.966 1824 2707 94 56 4681
Corrected 2 0.951 0.976 0.938 1947 3058 201 55 5261
Corrected 3 0.956 0.909 0.981 1813 3627 68 182 5690
Original 1 0.884 0.918 0.862 1732 2384 381 154 4651
Original 2 0.919 0.886 0.927 898 3909 309 115 5231
Original 3 0.830 0.928 0.778 1836 2876 822 143 5677
Antoine Vacavant 27 / 39

49. HCC detection by machine learning approaches
Work in progress: SVM classification
Multi-scale approach by considering all patch sizes
W/ and w/o preprocessing MRI enhancement
Patient # 1 Patient # 4 Patient # 16 Patient # 14
SVM+
featureselection
MRIcorrection+
SVM+
featureselection
Antoine Vacavant 28 / 39

50. HCC detection by machine learning approaches
Work in progress: DL
Use of new dataset (19 patients / 50+ MRI volumes)
Implementation of an improved program based on U-Net
Excellent global accuracy, but comparison should be detailed
Other architectures (CNN) and comparison
Need parallelization!
Small local cluster
Cloud computing
With Keras + Tensorflow framework
Antoine Vacavant 29 / 39

51. Outline
1. Me in one slide
2. Context and motivation
3. Ontology-based liver cancer diagnosis and treatment
4. Liver segmentation
5. Hepatic vascular network segmentation
6. Couinaud representation
7. HCC detection by machine learning approaches
8. Numerical simulation of hepatic blood flow
9. Conclusion and future works
Antoine Vacavant 30 / 39

52. Numerical simulation of hepatic blood flow
TACE
Following BCLC, TACE is the recommended treatment for
intermediate-staged HCC
TransArterial Chemo-Embolization
In our team, clinical research
Embolization process (bi-embolization)
Animal modeling of HCC and TACE experimentation
Tumor chemo-embolization
Catheter
HCC
Femoral
artery
Anticancer
agent
Aorta
Liver(a)
(b)
Antoine Vacavant 31 / 39

53. Numerical simulation of hepatic blood flow
TACE
Following BCLC, TACE is the recommended treatment for
intermediate-staged HCC
TransArterial Chemo-Embolization
In our team, clinical research
Embolization process (bi-embolization)
Animal modeling of HCC and TACE experimentation
Tumor chemo-embolization
Catheter
HCC
Femoral
artery
Anticancer
agent
Aorta
Liver(a)
(b)
Challenge
How to simulate numerically blood flow
within the liver?
Antoine Vacavant 31 / 39

54. Numerical simulation of hepatic blood flow
From an IRCAD sample, with CATIA
Reconstruct surfaces liver parenchyma, portal and hepatic veins
From binary images
Reconstruct tetrahedral volumes
M.-A. Lebre, K. Arrouk, A.-K. Vo Van, A. Leborgne, M. Grand-Brochier, P. Beaurepaire, A. Vacavant, B. Magnin, A. Abergel, P. Chabrot: Medical
image processing and numerical simulation for digital hepatic parenchymal blood flow. In SASHIMI@MICCAI, LNCS 10557, pages 99–108, Quebec,
Canada, 2017.
Antoine Vacavant 32 / 39

55. Numerical simulation of hepatic blood flow
From same sample, with Abaqus
3-D integration of liver components
Simulation of blood flow within liver parenchyma with Darcy’law
Some good local behavior
But not realistic complete blood flow
Blood pressure Blood flux
Antoine Vacavant 33 / 39

56. Outline
1. Me in one slide
2. Context and motivation
3. Ontology-based liver cancer diagnosis and treatment
4. Liver segmentation
5. Hepatic vascular network segmentation
6. Couinaud representation
7. HCC detection by machine learning approaches
8. Numerical simulation of hepatic blood flow
9. Conclusion and future works
Antoine Vacavant 34 / 39

57. Conclusion and future works
During this talk
Ontologies for information systems about HCC diagnosis and treatment
3-D liver and ineer vessels modeling for Couinaud representation
Automatic HCC detection by machine learning
Image-guided numerical simulation of hepatic perfusion
Antoine Vacavant 35 / 39

58. Conclusion and future works
During this talk
Ontologies for information systems about HCC diagnosis and treatment
3-D liver and ineer vessels modeling for Couinaud representation
Automatic HCC detection by machine learning
Image-guided numerical simulation of hepatic perfusion
Tech transfer
Connect software engineering with image analysis tools
Concretize collaborations with companies (in progress)
How to visualize and interact with results?
Antoine Vacavant 35 / 39

59. Conclusion and future works
Robust image (pre)processing
Complete machine learning approaches, for liver cancer detection
Collecting or simulating big data, as VIP [Glatard et al., 2013]
Explore deep learning algorithms
Evaluate accuracy by radioligical and histological approaches
Robust image processing
Simulation of
image data
Collecting
image data
at large scale
Preprocessing:
enhancement,
denoising
Processing:
segmentation,
description
High-level tasks,
machine learning
User’s decision,
evaluation
Antoine Vacavant 36 / 39

60. Conclusion and future works
In-silico trials
Efficient simulation of hepatic blood flow and drug delivery (TACE)
Robust image processing should lead to realistic outcomes
Call on physiology, biomechanics or biochemistry [Viceconti et al., 2016]
Use microscopic images to reconstruct finer vascular networks
Patient or
animal imaging
Preprocessing,
processing,
machine learning
Integration
within simulator
Finite ele-
ment analysis
Evaluation
of robustness
Antoine Vacavant 37 / 39

61. Conclusion and future works
R-VESSEL-X project
ANR Project 2019-2022 (42 months)
Robust vascular network extraction and
understanding within hepatic biomedical images
Segment liver vessels from MRI volumes
Extend vessels by machine learning from
Human CT data
Animal µ-MRI/synchrotron registration
Validation by numerical simulation of hepatic
perfusion
Antoine Vacavant 38 / 39

62. The end...
Questions?
Antoine Vacavant 39 / 39