@inproceedings{07a77ef0a54240d1b1f002528a149557,
title = "Abdominal Segmentation and Blood Vessel Detection in CTA Scans for Diep Reconstruction Procedures",
abstract = "Deep Inferior Epigastric Perforator (DIEP) flap reconstruction requires precise identification of perforator vessels traversing the abdominal muscle to supply the fat and skin tissues. Computed tomography angiography (CTA) reveals contrast-enhanced blood vessels critical for the DIEP surgical harvesting selection. CTA annotation is time-consuming and expertise-dependent, requiring clinicians to trace small perforator vessels through hundreds of image slices. We present an end-to-end automated pipeline that receives raw CTA scans and performs ROI extraction, deep learning multi-class segmentation and detection of perforation points. The pipeline achieves effective segmentation with reliable detection of critical anatomical landmarks. It can reduce pre-operative preparation time while maintaining clinical accuracy for improved surgical planning and navigation.",
keywords = "Breast reconstruction, CTA, DIEP, Deep Learning, Plastic surgical planning",
author = "Shir Ashkenazi and Nahum Kiryati and Dor Freidin and Ariel Tessone and Shai Tejman-Yarden and Arnaldo Mayer",
note = "Publisher Copyright: {\textcopyright} 2026 IEEE.; 23rd IEEE International Symposium on Biomedical Imaging, ISBI 2026 ; Conference date: 08-04-2026 Through 11-04-2026",
year = "2026",
doi = "10.1109/ISBI61048.2026.11515327",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
booktitle = "ISBI 2026 - 23rd IEEE International Symposium on Biomedical Imaging",
address = "United States",
}