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Updated: Sep 3, 2025

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

SemiCurv: Semi-Supervised Curvilinear Structure Segmentation.

Xun Xu, Manh Cuong Nguyen, Yasin Yazici

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|July 27, 2022

Summary

This summary is machine-generated.

SemiCurv, a semi-supervised learning framework, effectively segments curvilinear structures using readily available unlabeled data. This approach significantly reduces the need for expensive labeled data while maintaining high performance.

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Area of Science:

Computer Vision
Machine Learning
Medical Image Analysis

Background:

Curvilinear structure segmentation research has primarily focused on network architecture and loss functions.
The significant cost and labor involved in acquiring labeled data for segmentation tasks have been largely overlooked.
Unlabeled data is often abundant and accessible, presenting an opportunity for data-efficient learning.

Purpose of the Study:

To introduce SemiCurv, a novel semi-supervised learning (SSL) framework for curvilinear structure segmentation.
To leverage readily available unlabeled data to mitigate the expensive labeling burden in segmentation tasks.
To address key challenges in applying SSL to curvilinear segmentation, particularly data augmentation and class imbalance.

Main Methods:

Developed a semi-supervised learning framework (SemiCurv) for curvilinear structure segmentation.
Introduced a novel geometric transformation for strong data augmentation and used a differentiable inverse transformation for prediction alignment to compute pixel-wise consistency.
Proposed a N-pair consistency loss function to prevent collapsed predictions, especially in cases of severe class imbalance.

Main Results:

SemiCurv effectively utilizes unlabeled data, significantly reducing the requirement for labeled samples.
With only 5% labeled data, the framework achieved approximately 95% of the performance of fully supervised methods.
Evaluated on six diverse curvilinear segmentation datasets, demonstrating robust performance across different data types.

Conclusions:

SemiCurv offers a viable and efficient solution for curvilinear structure segmentation by effectively incorporating unlabeled data.
The proposed methods for data augmentation and loss functions successfully address challenges in semi-supervised segmentation.
This framework has the potential to substantially decrease the cost and effort associated with creating labeled datasets for segmentation tasks.