Leveraging Unlabeled Data for 3D Medical Image Segmentation Through Self-Supervised Contrastive Learning

IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1–5

Abstract

Current 3D semi-supervised segmentation methods face significant challenges such as limited consideration of contextual information and the inability to generate reliable pseudo-labels for effective unsupervised data use. To address these challenges, we introduce two distinct subnetworks designed to explore and exploit the discrepancies between them, ultimately correcting the erroneous prediction results. More specifically, we identify regions of inconsistent predictions and initiate a targeted verification training process. This procedure strategically fine-tunes and harmonizes the predictions of the subnetworks, leading to enhanced utilization of contextual information. Furthermore, to adaptively fine-tune the network’s representational capacity and reduce prediction uncertainty, we employ a self-supervised contrastive learning paradigm. For this, we use the network’s confidence to distinguish between reliable and unreliable predictions. The model is then trained to effectively minimize unreliable predictions. Our experimental results for organ segmentation, obtained from clinical MRI and CT scans, demonstrate the effectiveness of our approach when compared to state-of-the-art methods. The codebase is accessible on GitHub.

Authors 5

1. Sanaz Karimijafarbigloo

   University of Regensburg

   Affiliation as printed

   University of Regensburg,Faculty of Informatics and Data Science,Germany

2. Reza Azad Aachen Faculty of Electrical Engineering and Information Technology

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Faculty of Electrical Engineering and Information Technology,Germany

3. Yury Velichko

   Northwestern University

   Affiliation as printed

   Northwestern University,Machine and Hybrid Intelligence Lab,Chicago,IL,USA

4. Ulaş Bağcı

   Northwestern University

   Affiliation as printed

   Northwestern University,Machine and Hybrid Intelligence Lab,Chicago,IL,USA

5. Dorit Merhof

   University of Regensburg

   Affiliation as printed

   University of Regensburg,Faculty of Informatics and Data Science,Germany