Identification of Abdominal Wall Layers During Entry with an Optical Trocar
Computer Vision–Based Identification of Abdominal Wall Layers During Entry with an Optical Trocar
Establishing safe abdominal access is a critical first step in laparoscopic surgery. Optical trocars allow surgeons to visually monitor tissue layers during entry, aiming to reduce complications such as vascular injury, bowel perforation, or improper placement.
During insertion, the trocar traverses multiple abdominal wall layers—including skin, subcutaneous fat, fascia, muscle, and peritoneum—each with distinct but often subtle visual characteristics. Accurate real-time identification of these layers is essential for safe entry, yet remains challenging due to motion, deformation, bleeding, and limited field of view.
The aim of this project is to develop a computer vision system operating on optical trocar video to automatically identify, segment, or classify abdominal wall layers during entry. Using real surgical video data, students will design, implement, and evaluate vision-based methods that detect transitions between tissue layers and provide decision support for safe trocar placement.
Mentor Details:
Prof. Yoav Mintz
Mentor Details:
Requirments:
Students will aim to:
Analyze optical trocar video data and characterize visual features of abdominal wall layers
Develop a computer vision pipeline for tissue layer identification
Apply deep learning–based models (e.g., CNNs, Vision Transformers) to video frames or short clips
Leverage temporal information to detect layer transitions
Evaluate performance using appropriate computer vision metrics
Problem Statement
Optical trocar videos present unique challenges for computer vision systems:
Extremely dynamic motion during insertion
Rapid tissue deformation and compression
Blood, fat smearing, and fluid occlusions
Limited and unstable field of view
Visual similarity between adjacent tissue layers
Layer transitions (e.g., fascia → muscle → peritoneum) may occur within a few frames and can be difficult to distinguish even for experienced surgeons. The problem is to design a vision-based system that can reliably identify abdominal wall layers and detect key transitions in real time or offline analysis.
Project Objectives
Students will aim to:
Analyze optical trocar video data and characterize visual features of abdominal wall layers
Develop a computer vision pipeline for tissue layer identification
Apply deep learning–based models (e.g., CNNs, Vision Transformers) to video frames or short clips
Leverage temporal information to detect layer transitions
Evaluate performance using appropriate computer vision metrics
Technical Scope
The project may include one or more of the following tasks:
Frame-level or clip-level tissue classification (skin, fat, fascia, muscle, peritoneum)
Semantic segmentation of abdominal wall layers
Temporal modeling using optical flow, 3D CNNs, or recurrent architectures
Event detection for key milestones (e.g., peritoneal entry)
Weakly supervised learning using procedural timestamps or surgeon annotations
Required Knowledge and Prerequisites
Core Requirements
Familiarity with fundamental computer vision concepts
Experience with convolutional neural networks (CNNs)
Basic understanding of deep learning frameworks (e.g., PyTorch, TensorFlow)
Ability to work with image and video datasets
Recommended Background
Image classification and segmentation architectures
Video processing and temporal modeling
Model evaluation metrics (accuracy, F1-score, IoU, temporal precision)
No prior surgical knowledge is required; anatomical and procedural background will be provided.
Project Difficulty and Expected Level
Vision complexity: High (fast motion, occlusions, limited visibility)
Modeling complexity: Moderate to high
Domain knowledge: Low
This project is well-suited for:
Teams of 2–4 students
Expected Outcomes
A working computer vision prototype for abdominal wall layer identification
Quantitative evaluation on optical trocar video data
Analysis of failure cases (bleeding, motion blur, fat occlusion)
Well-documented code and a technical report