%0 Book Section
%A Khan, Abbas
%A Asad, Muhammad
%A Zolotarev, Alexander
%A Roney, Caroline
%A Mathur, Anthony
%A Benning, Martin
%A Slabaugh, Gregory
%B Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition: First Challenge
%C Cham, Switzerland
%D 2024
%E Pepe, Antonio
%E Melito, Gian Marco
%E Egger, Jan
%F discovery:10191148
%I Springer Nature Switzerland
%P 67-79
%S SEGA
%T Misclassification Loss for Segmentation of the Aortic Vessel Tree
%U https://discovery.ucl.ac.uk/id/eprint/10191148/
%V 14539
%X 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.
%Z This version is the author-accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.