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Related Experiment Video

Updated: Oct 14, 2025

A Full Skin Defect Model to Evaluate Vascularization of Biomaterials In Vivo

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Vessel-CAPTCHA: An efficient learning framework for vessel annotation and segmentation.

Vien Ngoc Dang¹, Francesco Galati², Rosa Cortese³

¹Data Science Department, EURECOM, Sophia Antipolis, France; Artificial Intelligence in Medicine Lab, Facultat de Matemátiques I Informática, Universitat de Barcelona, Spain.

Medical Image Analysis

|November 3, 2021

Summary

This summary is machine-generated.

This study introduces an efficient deep learning framework for segmenting 3D brain vessels, significantly reducing annotation time. The novel method uses weak patch-level labels, achieving state-of-the-art accuracy in cerebrovascular segmentation.

Keywords:

Cerebrovascular tree Deep learning Efficient annotation Segmentation Weak supervised learning

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Published on: November 30, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Deep learning for 3D brain vessel segmentation lags behind other organ segmentation due to challenges with small object detection and limited annotated data.
The complexity of vascular trees and the small size of vessels necessitate large annotated datasets for traditional deep learning methods.
Existing deep learning approaches struggle with the fine details and intricate structures of the cerebrovascular network.

Purpose of the Study:

To develop a novel, annotation-efficient deep learning framework for segmenting 3D brain vessels.
To overcome the limitations of pixel-wise annotations in deep learning for vessel segmentation.
To reduce the significant time and effort required for annotating training data in medical imaging.

Main Methods:

Proposed a framework utilizing weak patch-level labels for training, inspired by CAPTCHA-like human-bot differentiation.
Employed user-provided weak annotations to synthesize pixel-wise pseudo-labels for training a segmentation network.
Integrated a classifier network to generate additional weak labels and provide quality assessment for images.

Main Results:

Achieved state-of-the-art accuracy in segmenting the cerebrovascular tree from Time-of-Flight angiography (TOF) and Susceptibility-Weighted Images (SWI).
Demonstrated a significant reduction in annotation time by approximately 77% compared to methods requiring pixel-wise labels.
The framework effectively handles the challenges of segmenting small vessels and complex vascular structures.

Conclusions:

The developed annotation-efficient deep learning framework is highly effective for 3D brain vessel segmentation.
Weakly supervised learning significantly reduces the annotation burden without compromising segmentation accuracy.
This approach offers a practical solution for applying deep learning to challenging medical image segmentation tasks.