Khan, Abbas; Asad, Muhammad; Zolotarev, Alexander; Roney, Caroline; Mathur, Anthony; Benning, Martin; Slabaugh, Gregory; (2024) Misclassification Loss for Segmentation of the Aortic Vessel Tree. In: Pepe, Antonio and Melito, Gian Marco and Egger, Jan, (eds.) Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition: First Challenge. (pp. 67-79). Springer Nature Switzerland: Cham, Switzerland.

Text Benning_Misclassification_Loss.pdf - Accepted Version Access restricted to UCL open access staff until 11 February 2025. Download (6MB)

Abstract

Common pixel-based loss functions for image segmentation struggle with the fine-scale structures often found in the aortic vessel tree. In this paper, we propose a Misclassification Loss (MC loss) function, which can effectively suppress false positives and rescue the false negatives. A differentiable eXclusive OR (XOR) operation is implemented to identify these false predictions, which are then minimized through a cross-entropy loss. The proposed MC loss helps the network achieve better performance by focusing on these difficult regions. On the Segmentation of the Aorta SEG.A. 2023 challenge, our method achieves a Dice score of 0.93 and a Hausdorff Distance (HD) of 3.50 mm on a 5-fold split of 56 training subjects. We participated in the SEG.A. 2023 challenge, and the proposed method ranks among the top-six approaches in the validation phase-1. The pre-trained models, source code, and implementation will be made public.

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