Towards Automatic Cartilage Quantification in Clinical Trials – Continuing from the 2019 IWOAI Knee Segmentation Challenge
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Towards Automatic Cartilage Quantification in Clinical Trials – Continuing from the 2019 IWOAI Knee Segmentation Challenge. / Dam, Erik B; Desai, Arjun D; Deniz, Cem M; Rajamohan, Haresh R; Regatte, Ravinder; Iriondo, Claudia; Pedoia, Valentina; Majumdar, Sharmila; Perslev, Mathias; Igel, Christian; Pai, Akshay; Gaj, Sibaji; Yang, Mingrui; Nakamura, Kunio; Li, Xiaojuan; Maqbool, Hasan; Irmakci, Ismail; Song, Sang-Eun; Bagci, Ulas; Hargreaves, Brian; Gold, Garry; Chaudhari, Akshay.
In: Osteoarthritis Imaging, Vol. 3, No. 1, 100087, 2023.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Towards Automatic Cartilage Quantification in Clinical Trials – Continuing from the 2019 IWOAI Knee Segmentation Challenge
AU - Dam, Erik B
AU - Desai, Arjun D
AU - Deniz, Cem M
AU - Rajamohan, Haresh R
AU - Regatte, Ravinder
AU - Iriondo, Claudia
AU - Pedoia, Valentina
AU - Majumdar, Sharmila
AU - Perslev, Mathias
AU - Igel, Christian
AU - Pai, Akshay
AU - Gaj, Sibaji
AU - Yang, Mingrui
AU - Nakamura, Kunio
AU - Li, Xiaojuan
AU - Maqbool, Hasan
AU - Irmakci, Ismail
AU - Song, Sang-Eun
AU - Bagci, Ulas
AU - Hargreaves, Brian
AU - Gold, Garry
AU - Chaudhari, Akshay
PY - 2023
Y1 - 2023
N2 - Objective: To evaluate whether the deep learning (DL) segmentation methods from the six teams that participated in the IWOAI 2019 Knee Cartilage Segmentation Challenge are appropriate for quantifying cartilage lossin longitudinal clinical trials.Design: We included 556 subjects from the Osteoarthritis Initiative study with manually read cartilage volumescores for the baseline and 1-year visits. The teams used their methods originally trained for the IWOAI 2019challenge to segment the 1130 knee MRIs. These scans were anonymized and the teams were blinded to anysubject or visit identifiers. Two teams also submitted updated methods. The resulting 9,040 segmentations areavailable online.The segmentations included tibial, femoral, and patellar compartments. In post-processing, we extractedmedial and lateral tibial compartments and geometrically defined central medial and lateral femoral subcompartments. The primary study outcome was the sensitivity to measure cartilage loss as defined by the standardized response mean (SRM).Results: For the tibial compartments, several of the DL segmentation methods had SRMs similar to the goldstandard manual method. The highest DL SRM was for the lateral tibial compartment at 0.38 (the gold standardhad 0.34). For the femoral compartments, the gold standard had higher SRMs than the automatic methods at0.31/0.30 for medial/lateral compartments.Conclusion: The lower SRMs for the DL methods in the femoral compartments at 0.2 were possibly due to thesimple sub-compartment extraction done during post-processing. The study demonstrated that state-of-the-artDL segmentation methods may be used in standardized longitudinal single-scanner clinical trials for well-definedcartilage compartments.
AB - Objective: To evaluate whether the deep learning (DL) segmentation methods from the six teams that participated in the IWOAI 2019 Knee Cartilage Segmentation Challenge are appropriate for quantifying cartilage lossin longitudinal clinical trials.Design: We included 556 subjects from the Osteoarthritis Initiative study with manually read cartilage volumescores for the baseline and 1-year visits. The teams used their methods originally trained for the IWOAI 2019challenge to segment the 1130 knee MRIs. These scans were anonymized and the teams were blinded to anysubject or visit identifiers. Two teams also submitted updated methods. The resulting 9,040 segmentations areavailable online.The segmentations included tibial, femoral, and patellar compartments. In post-processing, we extractedmedial and lateral tibial compartments and geometrically defined central medial and lateral femoral subcompartments. The primary study outcome was the sensitivity to measure cartilage loss as defined by the standardized response mean (SRM).Results: For the tibial compartments, several of the DL segmentation methods had SRMs similar to the goldstandard manual method. The highest DL SRM was for the lateral tibial compartment at 0.38 (the gold standardhad 0.34). For the femoral compartments, the gold standard had higher SRMs than the automatic methods at0.31/0.30 for medial/lateral compartments.Conclusion: The lower SRMs for the DL methods in the femoral compartments at 0.2 were possibly due to thesimple sub-compartment extraction done during post-processing. The study demonstrated that state-of-the-artDL segmentation methods may be used in standardized longitudinal single-scanner clinical trials for well-definedcartilage compartments.
U2 - 10.1016/j.ostima.2023.100087
DO - 10.1016/j.ostima.2023.100087
M3 - Journal article
VL - 3
JO - Osteoarthritis Imaging
JF - Osteoarthritis Imaging
IS - 1
M1 - 100087
ER -
ID: 335955358