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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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A temporal convolutional network-based approach and a benchmark dataset for colonoscopy video temporal segmentation.

Carlo Biffi¹, Giorgio Roffo¹, Pietro Salvagnini¹

¹Cosmo Intelligent Medical Devices, Dublin, Ireland.

Computer Methods and Programs in Biomedicine

|July 9, 2025

Summary

This summary is machine-generated.

This study introduces ColonTCN, a novel deep learning model for segmenting colonoscopy videos into anatomical sections and procedural phases. The developed open-access dataset and ColonTCN model advance automated colonoscopy reporting and computer-aided diagnosis.

Keywords:

Automated reporting Colonoscopy Dataset Temporal convolutional networks Temporal video segmentation

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Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence in Medicine

Background:

Advancements in computer-aided detection and diagnosis systems for colonoscopy are driving the need for automated reporting.
Accurate segmentation of colonoscopy videos into anatomical sections and procedural phases is crucial for developing these systems.
Existing research has not sufficiently addressed the creation of datasets and models for this specific temporal segmentation task.

Purpose of the Study:

To create the first open-access dataset for temporal segmentation of full-procedure colonoscopy videos.
To propose and evaluate a state-of-the-art deep learning approach, ColonTCN, for this segmentation task.
To benchmark ColonTCN against competitive models and provide insights into the challenges of colonoscopy video segmentation.

Main Methods:

Annotated the REAL-Colon dataset (2.7 million frames, 60 videos) with frame-level labels for anatomical locations and procedural phases.
Developed ColonTCN, a temporal convolutional network architecture designed for efficient temporal dependency capture in videos.
Implemented a dual k-fold cross-validation protocol for robust model evaluation on unseen, multi-center data.

Main Results:

ColonTCN achieved state-of-the-art classification accuracy with a low parameter count.
The model outperformed competitive approaches in both k-fold cross-validation settings.
Ablation studies confirmed the effectiveness of custom temporal convolutional blocks in enhancing learning and model efficiency.

Conclusions:

The proposed open-access benchmark and ColonTCN represent a significant step forward in the temporal segmentation of colonoscopy procedures.
This work facilitates further open-access research to meet the clinical need for automated colonoscopy analysis.
The code and dataset are publicly available to support the research community.