TY  - CHAP
M1  - 14539
Y1  - 2024/01/01/
T3  - SEGA
ID  - discovery10191148
UR  - https://doi.org/10.1007/978-3-031-53241-2_6
N2  - 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.
SP  - 67
ED  - Pepe, Antonio
ED  - Melito, Gian Marco
ED  - Egger, Jan
PB  - Springer Nature Switzerland
A1  - Khan, Abbas
A1  - Asad, Muhammad
A1  - Zolotarev, Alexander
A1  - Roney, Caroline
A1  - Mathur, Anthony
A1  - Benning, Martin
A1  - Slabaugh, Gregory
AV  - public
TI  - Misclassification Loss for Segmentation of the Aortic Vessel Tree
CY  - Cham, Switzerland
T2  - Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition: First Challenge
EP  - 79
N1  - This version is the author-accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ER  -