Ceranka, J., Lamtenzan, D., Boonen, P., Kapila, A., Brussaard, C., Hamdi, M., & Vandemeulebroucke, J. (2026). Computer-Assisted Surgical Planning for DIEP Flap Breast Reconstruction Surgery. In T. Zhang, O. L. Saldanha, L. Han, N. Rasoolzadeh, L. Garrucho Moras, J. van Dijk, T. Tan, J. N. Kather, & R. Mann (Eds.), Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care - 2nd Deep Breast Workshop, Deep-Breath 2025, Held in Conjunction with MICCAI 2025, Proceedings (pp. 123-133). (Lecture Notes in Computer Science; Vol. 16142 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-032-05559-0_13

@inproceedings{137310ebfa2e4f35b7b3079496deff9b,
title = "Computer-Assisted Surgical Planning for DIEP Flap Breast Reconstruction Surgery",
abstract = "The deep inferior epigastric artery perforator (DIEP) flap is the gold standard for autologous breast reconstruction following mastectomy, and involving transplantation of abdominal skin and fat while preserving muscle integrity. A critical yet challenging aspect of surgical planning is the accurate identification of perforator vessels - small arteries supplying blood to the transplanted tissue slab. Manual perforator selection from computed tomography angiography (CTA) scans is time-consuming, prone to error, and frequently resulting in intra-operative adjustments, prolonged surgery and increased risk of complications. We propose a fully-automated pre-operative planning framework designed to reliably map perforator vessels in CTA images. The system consists of a depth-aware annotation strategy that corrects spatial distortions associated with maximum intensity projection imaging; a patient-specific, anatomy-aware region of interest selection; and a deep-learning perforator segmentation pipeline leveraging a Swin UNETR architecture, trained with custom continuity-aware loss function and region-specific patch sampling. The method was validated on clinical CTA scans from 55 patients, using expert-guided annotations of the perforator vessels. The proposed method achieved a perforator segmentation median Dice similarity coefficient of 0.60 and a 95th-percentile Hausdorff distance of less than 18 mm, significantly improving vessel segmentation accuracy in fat regions over baseline Swin UNETR. These results demonstrate the clinical feasibility of automated segmentation, which has the potential to standardise surgical planning, enhance perforator selection precision, and ultimately improve patient outcomes in DIEP flap breast reconstruction.",
keywords = "breast cancer, computer-assisted surgical planning, deep learning, DIEP flap surgery, perforator segmentation",
author = "Jakub Ceranka and Diego Lamtenzan and Pieter Boonen and Ayush Kapila and Carola Brussaard and Moustapha Hamdi and Jef Vandemeulebroucke",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; 2nd Deep Breast Workshop on Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care, Deep-Breath 2025, held in conjunction with the 28th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2025 ; Conference date: 23-09-2025 Through 23-09-2025",
year = "2026",
doi = "10.1007/978-3-032-05559-0\_13",
language = "English",
isbn = "9783032055583",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "123--133",
editor = "Tianyu Zhang and Saldanha, \{Oliver Lester\} and Luyi Han and Nika Rasoolzadeh and \{Garrucho Moras\}, Lidia and \{van Dijk\}, Jarek and Tao Tan and Kather, \{Jakob Nikolas\} and Ritse Mann",
booktitle = "Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care - 2nd Deep Breast Workshop, Deep-Breath 2025, Held in Conjunction with MICCAI 2025, Proceedings",
address = "Germany",
}