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TypeSeg: A type-aware encoder-decoder network for multi-type ultrasound images co-segmentation.

Fang Chen¹, Haoran Ye¹, Daoqiang Zhang¹

¹College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, China.

Computer Methods and Programs in Biomedicine

|December 25, 2021

Summary

This study introduces TypeSeg, a novel network for segmenting multiple ultrasound image types. TypeSeg effectively distinguishes between tissue types, improving co-segmentation accuracy for medical imaging applications.

Keywords:

Encoder-decoder network Multi-type ultrasound images Type-aware information

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Area of Science:

Medical Imaging
Computer Vision
Biomedical Engineering

Background:

Ultrasound is a portable, radiation-free imaging technique suitable for diverse tissue structures.
Current ultrasound segmentation methods often fail to address multi-type image co-segmentation or ignore type-specific information.

Purpose of the Study:

To develop a novel type-aware encoder-decoder network (TypeSeg) for multi-type ultrasound image co-segmentation.
To address limitations in existing methods that focus on single image types or overlook type-aware details.

Main Methods:

Proposed a type-aware encoder-decoder network (TypeSeg) for co-segmentation of multi-type ultrasound images.
Developed a type-aware metric learning module to optimize feature representation, ensuring similar types are clustered and dissimilar types are separated.
Integrated a decision module to identify common tissue types, guiding the encoder-decoder network for accurate segmentation mask generation.

Main Results:

Evaluated TypeSeg on an ultrasound dataset comprising four tissue types.
Achieved a mean Intersection over Union (IOU) score of 87.51% ± 3.93% for multi-type ultrasound image co-segmentation.
Demonstrated superior performance compared to existing state-of-the-art methods.

Conclusions:

The proposed TypeSeg method significantly outperforms current algorithms in multi-type ultrasound image co-segmentation.
The type-aware approach is effective for enhancing segmentation accuracy in complex ultrasound datasets.