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IMIIN: An inter-modality information interaction network for 3D multi-modal breast tumor segmentation.

Chengtao Peng¹, Yue Zhang², Jian Zheng³

¹Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230026, China; Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|December 3, 2021

Summary

This study introduces a novel 3D inter-modality information interaction network (IMIIN) for segmenting breast tumors in multi-modal MRI. The new method enhances segmentation accuracy, particularly for small tumors, showing promise for clinical applications.

Keywords:

Breast tumor segmentation Deep learning Inter-modality information interaction Multi-modal

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Accurate breast tumor segmentation is vital for cancer diagnosis and treatment.
Multi-modal MRI offers complementary information for tumor morphology analysis.
Existing methods struggle with efficient multi-modal fusion and small tumor segmentation.

Purpose of the Study:

To develop a 3D inter-modality information interaction network (IMIIN) for improved breast tumor segmentation in multi-modal MRI.
To enhance the handling of small tumor characteristics and boundary details.
To enable effective and targeted information exchange between different imaging modalities.

Main Methods:

A hierarchical structure for extracting local information of small tumors.
A 3D tiny object segmentation network based on DenseVoxNet for preserving boundary details.
A bi-directional request-supply information interaction module for inter-modality data fusion.

Main Results:

The proposed 3D IMIIN achieved superior segmentation results compared to state-of-the-art methods on a clinical dataset.
The network demonstrated enhanced precision in segmenting tumor boundaries, especially for small tumors.
The bi-directional interaction module effectively managed information exchange between modalities.

Conclusions:

The novel 3D IMIIN offers a significant advancement in multi-modal breast tumor segmentation.
The method's ability to precisely segment small tumors and their boundaries holds strong clinical potential.
Effective inter-modality information interaction is key to improving segmentation accuracy.