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Updated: Nov 15, 2025

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

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Published on: November 30, 2022

Label cleaning and propagation for improved segmentation performance using fully convolutional networks.

Takaaki Sugino¹, Yutaro Suzuki¹, Taichi Kin²

¹Department of Biomedical Information, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, Tokyo, Japan.

International Journal of Computer Assisted Radiology and Surgery

|March 3, 2021

Summary

This summary is machine-generated.

Fully convolutional networks (FCNs) can effectively clean noisy and interpolate labels from sparse medical image annotations. This approach improves segmentation performance, even with incomplete training data, paving the way for more efficient annotation processes.

Keywords:

Fully convolutional networks Label cleaning Label propagation Segmentation

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Area of Science:

Medical Image Analysis
Deep Learning

Background:

Fully Convolutional Networks (FCNs) are increasingly used for medical image segmentation.
Acquiring large, high-quality annotated datasets for training FCNs is a significant challenge.

Purpose of the Study:

To evaluate the capability of FCNs in cleaning noisy labels and interpolating missing information from sparse annotations.
To assess the performance of FCNs in medical image segmentation using incomplete training data.

Main Methods:

Utilized 2D and 3D FCNs for volumetric brain segmentation from MRI data.
Trained networks on simulated incomplete datasets with noisy and sparse annotations.

Main Results:

Both 2D and 3D FCNs demonstrated improved segmentation accuracy with incomplete training data.
Employing three orthogonal annotation images significantly enhanced network training and segmentation outcomes.

Conclusions:

FCNs show potential for effective label cleaning and propagation in medical image segmentation.
FCNs can achieve robust segmentation performance even with sparse and potentially noisy manual annotations, suggesting a more efficient annotation workflow.