Revisiting the supervision level in semi-supervised learning for automated tumor segmentation: application to lymphoma FDG PET imaging

Fereshteh Yousefirizi; Joo Hyun O; Ingrid Bloise; Amirhosein Toosi; Carlos F. Uribe; Arman Rahmim

doi:10.1117/12.2654492

3 April 2023 Revisiting the supervision level in semi-supervised learning for automated tumor segmentation: application to lymphoma FDG PET imaging

Fereshteh Yousefirizi, Joo Hyun O, Ingrid Bloise, Amirhosein Toosi, Carlos F. Uribe, Arman Rahmim

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Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 124643C (2023) https://doi.org/10.1117/12.2654492
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

The need for accurate and consistent ground truth hinders advances in supervised learning approaches for tumor segmentation especially in PET images. In this study, we revisited the effect of supervision level on two semi-supervised approaches based on Robust FCM (RFCM) and Mumford-Shah (MS) losses for unsupervised learning combined with labeled FCM (LFCM) and Dice loss respectively as the supervised loss terms ((RFCM + αLFCM) and (MS+ αDice)). We used a multi-center (BC and SM) dataset of lymphoma patients with heterogeneous characteristics. Our results revealed that when the test data are from a center with low contribution in training data, increasing the level of supervision results in lower segmentation performance. The performance drop of MS based semi-supervised approach was higher compared to FCM based that means the training of MS based approach is more dependent on supervised learning.

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Fereshteh Yousefirizi, Joo Hyun O, Ingrid Bloise, Amirhosein Toosi, Carlos F. Uribe, and Arman Rahmim "Revisiting the supervision level in semi-supervised learning for automated tumor segmentation: application to lymphoma FDG PET imaging", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124643C (3 April 2023); https://doi.org/10.1117/12.2654492

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