Presentation of the organizers

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SegTHOR challenge:

Segmentation of THoracic Organs at Risk in CT images

Caroline Petitjean, Zoé Lambert, Su Ruan

IEEE ISBI April 8th, 2019

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Presentation of the organizers

Su Ruan Professor at the University of Rouen Zoé Lambert Research Engineer at LMI, INSA Rouen Bernard Dubray MD, PhD, Radiotherapist at the Centre

Henri Becquerel, Rouen

Caroline Petitjean Associate Prof at the University of Rouen Rouen is located in Normandy, France

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Thoracic cancer and radiotherapy

Radiotherapy planning: design radiation beams such that they destroy the tumor and spare healthy Organ At Risk (OAR)

→ Need to precisely identify the OAR

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Segmenting thoracic OAR in CT images

Thoracic OAR: esophagus, heart, trachea and aorta Manual contouring requires 30 minutes

Achallenging task

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Volume rendering of labeled OAR from CT images

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Example: 228 CT slices and labeled OAR

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Focus on the esophagus

Esophagus: tube from throat to stomach Contours hardly visible, variable shape

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Our previous work

Baseline Dice scores:

[Trullo et al., ISBI 2017] [Trullo et al, SPIE JMI, 2019]

Esophagus 0.67-0.71

Heart 0.90

Trachea 0.82-0.83

Aorta 0.86

Protocol: 5-fold CV on 30 patients BResults not obtained on the

SegTHOR test dataset!

Why the SegTHOR challenge?

Room for improvement is left!

More generally: contributions multilabel image segmentation There is no public dataset with these OAR

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Other related challenge

AAPM: American Association of Physicists in Medicine AAPM Challenge (2017): esophagus,heart,lungs,spinal cord Training: 30 patients, testing: 12 patients

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The SegTHOR dataset

60 patients withlung cancer or Hodgkin lymphomaprovided by the anti-cancer center Henri Becquerel, Rouen, France 512_×512_×(150_∼284) voxels

usual resolution: 0.98_×0.98_×2.5 mm³ Training Set

40 patients (7390 slices)

NIfTI images 4 OAR Label maps

Test Set

20 patients(3694 slices)

NIfTI images only

Automated evaluation via CodaLab

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Evaluation measures: Dice metric and Hausdorff distance

For each of the 4 OAR, two metrics:

DM = 2^A_A^∩₊^B_B

HD = max(d(a,B), d(A,b))

→ Each of the 8 metrics is ranked.

Final rank: mean of the 8 ranks

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Timeline of the challenge

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A few figures about the challengers

Initial registration +500 Signed registration 149 Test set submission 50

Average submission numbers on the test set: 5.4_±3.1

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Paper submission

12 teams submitted a paper:

China: 6 teams (Shanghai, Hong-Kong, Sichuan) Russia: 2 teams

Germany, India, The Netherlands, South Korea: 1 team

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The 12 methods

Common features:

All methods are based on CNN: UNet, VNet, etc

Differences:

Different loss functions (Dice, Tversky, Cross-entropy) Full 3D vs. 2/3D vs 2D

Multiscale, multiresolution, multitask Postprocessing

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Schedule of the workshop

7 presentations by:

S. Kim et al Yonsei University, Yonsei University College of MedicineSouth Korea

#34 V. Kondratenko et al Skolkovo Institute of Science and TechnologyRussia #26

D. Lachinov Intel, Nizhny NovgorodRussia #15

L. van Harten et al Image Sciences Institute & Department of Radiother- apy, UMC UtrechtThe Netherlands

#12 BREAK from 10:30 until 11:00

S. Vesal et al Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

#9 Q. Wang et al Chinese University of HK, Sun Yat-sen University,

University of Hong KongChina

#5 M. Han et al Shanghai United Imaging Intelligence Inc.China #1

Summary of the results and future work Discussion (end at 12:15)