Deep learning in ventral hernia imaging: automated multi-structure CT segmentation for surgical planning.

Journal of abdominal wall surgery : JAWS Pub Date : 2026-08-07 eCollection Date: 2026-01-01 DOI:10.3389/jaws.2026.15545

Vinayak Rengan, Pravin Meenashi Sundaram, Eham Arora, Sabari Girieasen, Ashvind Bawa, Naveen Alexander, Rengan Ravanasamudram Sitaraman, Vimalakar Reddy, Vishakha Kalikar, Aman Arora, Lakshmi Kona, Rochita Venkataramanan, Devansh Lalwani, Dakshin Meenashi Sundaram, Rohit Kalla

{"title":"Deep learning in ventral hernia imaging: automated multi-structure CT segmentation for surgical planning.","authors":"Vinayak Rengan, Pravin Meenashi Sundaram, Eham Arora, Sabari Girieasen, Ashvind Bawa, Naveen Alexander, Rengan Ravanasamudram Sitaraman, Vimalakar Reddy, Vishakha Kalikar, Aman Arora, Lakshmi Kona, Rochita Venkataramanan, Devansh Lalwani, Dakshin Meenashi Sundaram, Rohit Kalla","doi":"10.3389/jaws.2026.15545","DOIUrl":null,"url":null,"abstract":"Background: Accurate preoperative assessment of ventral hernia defects remains time-intensive and subject to inter-observer variability. Current manual CT analysis for surgical planning is time-consuming, with inconsistent measurements affecting operative decision-making.Methods: 215 CT scans of adults with ventral hernias were analyzed using TransUNet-inspired deep learning models. Expert annotations of anatomical landmarks and hernia features served as ground truth. Models were trained to automate segmentation of hernia defects and other critical anatomical structures.Results: Automated segmentation achieved IoU values of 0.85 for hernia defects, 0.89 for rectus abdominis muscles, 0.87 for lateral abdominal wall muscles, and 0.91 for psoas muscles.Conclusion: Deep learning automation provides rapid, standardized hernia assessment for surgical planning. The system delivers objective measurements with significant time savings, demonstrating technical feasibility as a proof-of-concept that warrants further prospective clinical validation before deployment in operative decision-making.","PeriodicalId":516856,"journal":{"name":"Journal of abdominal wall surgery : JAWS","volume":"5 ","pages":"15545"},"PeriodicalIF":0.0000,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13493322/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of abdominal wall surgery : JAWS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/jaws.2026.15545","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

Abstract

Background: Accurate preoperative assessment of ventral hernia defects remains time-intensive and subject to inter-observer variability. Current manual CT analysis for surgical planning is time-consuming, with inconsistent measurements affecting operative decision-making.

Methods: 215 CT scans of adults with ventral hernias were analyzed using TransUNet-inspired deep learning models. Expert annotations of anatomical landmarks and hernia features served as ground truth. Models were trained to automate segmentation of hernia defects and other critical anatomical structures.

Results: Automated segmentation achieved IoU values of 0.85 for hernia defects, 0.89 for rectus abdominis muscles, 0.87 for lateral abdominal wall muscles, and 0.91 for psoas muscles.

Conclusion: Deep learning automation provides rapid, standardized hernia assessment for surgical planning. The system delivers objective measurements with significant time savings, demonstrating technical feasibility as a proof-of-concept that warrants further prospective clinical validation before deployment in operative decision-making.

查看原文本刊更多论文

腹疝成像中的深度学习：用于手术计划的自动多结构CT分割。

背景：腹疝缺损的准确术前评估仍然是费时的，并且受观察者之间的差异。目前手工CT分析手术计划是耗时的，不一致的测量影响手术决策。方法：使用transunet启发的深度学习模型对215例成人腹疝CT扫描进行分析。解剖标志和疝气特征的专家注释作为基础事实。训练模型以自动分割疝缺损和其他关键解剖结构。结果：疝缺损自动分割IoU值为0.85，腹直肌为0.89，腹外壁肌为0.87，腰肌为0.91。结论：深度学习自动化为手术计划提供快速、标准化的疝评估。该系统提供了客观的测量结果，节省了大量的时间，证明了技术可行性，作为概念验证，在部署到手术决策之前，需要进一步的前瞻性临床验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of abdominal wall surgery : JAWS

自引率

0.00%

发文量