eprintid: 10191148 rev_number: 10 eprint_status: archive userid: 699 dir: disk0/10/19/11/48 datestamp: 2024-04-22 10:56:35 lastmod: 2025-02-11 07:10:05 status_changed: 2024-04-22 10:56:35 type: book_section metadata_visibility: show sword_depositor: 699 creators_name: Khan, Abbas creators_name: Asad, Muhammad creators_name: Zolotarev, Alexander creators_name: Roney, Caroline creators_name: Mathur, Anthony creators_name: Benning, Martin creators_name: Slabaugh, Gregory title: Misclassification Loss for Segmentation of the Aortic Vessel Tree ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F48 note: This version is the author-accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. 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. date: 2024-01-01 date_type: published publisher: Springer Nature Switzerland official_url: https://doi.org/10.1007/978-3-031-53241-2_6 oa_status: green full_text_type: other language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2253344 doi: 10.1007/978-3-031-53241-2_6 isbn_13: 9783031532405 lyricists_name: Benning, Martin lyricists_id: MBENN44 actors_name: Benning, Martin actors_name: Kaltenbacher, Brigitte G actors_id: MBENN44 actors_id: BGKAL87 actors_role: owner actors_role: impersonator full_text_status: public series: SEGA volume: 14539 place_of_pub: Cham, Switzerland pagerange: 67-79 book_title: Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition: First Challenge editors_name: Pepe, Antonio editors_name: Melito, Gian Marco editors_name: Egger, Jan citation: 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. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10191148/1/Benning_Misclassification_Loss.pdf