@incollection{discovery10191148,
         address = {Cham, Switzerland},
          editor = {Antonio Pepe and Gian Marco Melito and Jan Egger},
       booktitle = {Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition: First Challenge},
       publisher = {Springer Nature Switzerland},
           month = {January},
          volume = {14539},
           pages = {67--79},
            note = {This version is the author-accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
          series = {SEGA},
            year = {2024},
           title = {Misclassification Loss for�Segmentation of�the�Aortic Vessel Tree},
          author = {Khan, Abbas and Asad, Muhammad and Zolotarev, Alexander and Roney, Caroline and Mathur, Anthony and Benning, Martin and Slabaugh, Gregory},
        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.},
             url = {https://doi.org/10.1007/978-3-031-53241-2\%5f6}
}