Segmentation of Infrared Breast Images Using MultiResUnet Neural Network

TL;DR

This paper demonstrates that MultiResUnet, a deep learning segmentation model, achieves high accuracy in automatically segmenting breast areas in infrared images, aiding noninvasive breast cancer screening.

Contribution

The study applies a state-of-the-art MultiResUnet model to breast IR image segmentation, showing improved accuracy over previous autoencoder-based methods.

Findings

MultiResUnet achieved 91.47% accuracy in segmentation.

It outperformed previous autoencoder models by about 2%.

The method facilitates faster, automated breast IR image analysis.

Abstract

Breast cancer is the second leading cause of death for women in the U.S. Early detection of breast cancer is key to higher survival rates of breast cancer patients. We are investigating infrared (IR) thermography as a noninvasive adjunct to mammography for breast cancer screening. IR imaging is radiation-free, pain-free, and non-contact. Automatic segmentation of the breast area from the acquired full-size breast IR images will help limit the area for tumor search, as well as reduce the time and effort costs of manual segmentation. Autoencoder-like convolutional and deconvolutional neural networks (C-DCNN) had been applied to automatically segment the breast area in IR images in previous studies. In this study, we applied a state-of-the-art deep-learning segmentation model, MultiResUnet, which consists of an encoder part to capture features and a decoder part for precise localization. It was used to segment the breast area by using a set of breast IR images, collected in our pilot study by imaging breast cancer patients and normal volunteers with a thermal infrared camera (N2 Imager). The database we used has 450 images, acquired from 14 patients and 16 volunteers. We used a thresholding method to remove interference in the raw images and remapped them from the original 16-bit to 8-bit, and then cropped and segmented the 8-bit images manually. Experiments using leave-one-out cross-validation (LOOCV) and comparison with the ground-truth images by using Tanimoto similarity show that the average accuracy of MultiResUnet is 91.47%, which is about 2% higher than that of the autoencoder. MultiResUnet offers a better approach to segment breast IR images than our previous model.