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Updated: Mar 29, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

LTPNet: Lesion-Aware Triple-Path Feature Fusion Network for Skin Lesion Segmentation.

Yange Sun^1,2, Sen Chen^1,2, Huaping Guo^1,2

¹School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.

Journal of Imaging

|March 27, 2026

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A new deep learning model, the lesion-aware triple-path feature fusion network (LTPNet), improves skin lesion segmentation accuracy. This advanced method enhances clinical decision support by overcoming challenges like complex backgrounds and low contrast.

Area of Science:

Medical image analysis
Computer vision
Artificial intelligence in healthcare

Background:

Accurate skin lesion segmentation is crucial for diagnosis but challenged by complex backgrounds, ambiguous boundaries, and low contrast.
Existing methods struggle with precise delineation, necessitating advanced deep learning approaches.

Purpose of the Study:

To introduce the lesion-aware triple-path feature fusion network (LTPNet) for improved skin lesion segmentation.
To address limitations in current segmentation techniques by enhancing feature extraction, refinement, and aggregation.

Main Methods:

The proposed LTPNet framework utilizes an end-to-end approach with distinct extraction, refinement, and aggregation stages.
Key modules include general foreground-background attention, attentive spatial modulator (ASM), lesion-aware lite-gate attention (LALGA), and triple-path feature fusion (TPFF).

Keywords:

attentive spatial modulator lesion-aware lite-gate attention skin lesion segmentation triple-path feature fusion

Related Experiment Videos

Last Updated: Mar 29, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

TPFF employs common, saliency, and difference paths to model multi-scale feature relationships.

Main Results:

LTPNet demonstrated superior segmentation accuracy on both in-domain and cross-domain datasets.
The model achieved competitive results with reasonable inference efficiency and manageable model complexity.
Experiments confirmed the effectiveness of the proposed attention and fusion modules in enhancing segmentation performance.

Conclusions:

LTPNet offers an effective solution for accurate and reliable skin lesion segmentation.
The proposed network shows significant potential for clinical decision support systems.
The lesion-aware design and multi-path fusion strategy contribute to robust performance across diverse datasets.