WDTISeg: One-Stage Interactive Segmentation for Breast Ultrasound Image Using Weighted Distance Transform and Shape-Aware Compound Loss

Xiaokang Li,Mengyun Qiao,Yi Guo,Yuanyuan Wang,Jin Zhou,Shichong Zhou,Cai Chang

doi:10.3390/app11146279

Xiaokang Li, Mengyun Qiao + Show 5 more

Open Access

https://doi.org/10.3390/app11146279

Copy DOI

Abstract

Accurate tumor segmentation is important for aided diagnosis using breast ultrasound. Interactive segmentation methods can obtain highly accurate results by continuously optimizing the segmentation result via user interactions. However, traditional interactive segmentation methods usually require a large number of interactions to make the result meet the requirements due to the performance limitations of the underlying model. With greater ability in extracting image information, convolutional neural network (CNN)-based interactive segmentation methods have been shown to effectively reduce the number of user interactions. In this paper, we proposed a one-stage interactive segmentation framework (interactive segmentation using weighted distance transform, WDTISeg) for breast ultrasound image using weighted distance transform and shape-aware compound loss. First, we used a pre-trained CNN to attain an initial automatic segmentation, based on which the user provided interaction points of mis-segmented areas. Then, we combined Euclidean distance transform and geodesic distance transform to convert interaction points into weighted distance maps to transfer segmentation guidance information to the model. The same CNN accepted the input image, the initial segmentation, and weighted distance maps as a concatenation input and provided a refined result, without another additional segmentation network. In addition, a shape-aware compound loss function using prior knowledge was designed to reduce the number of user interactions. In the testing phase on 200 cases, our method achieved a dice of 82.86 ± 16.22 (%) for automatic segmentation task and a dice of 94.45 ± 3.26 (%) for interactive segmentation task after 8 interactions. The results of comparative experiments proved that our method could obtain higher accuracy with fewer simple interactions than other interactive segmentation methods.

Highlights

Breast cancer is one of the leading causes of death in women around the world and diagnosing breast cancer in its early stages will always remain crucial [1,2]
The training process was greatly simplified because no additional automatic segmentation network was required to provide the initial results; (2) We proposed to convert user interactions into maps with weighted distance transform which combines geodesic distance and Euclidean distance transforms
We proposed a one-stage interactive segmentation framework (WDTISeg) for breast ultrasound image segmentation

Summary

Introduction

Breast cancer is one of the leading causes of death in women around the world and diagnosing breast cancer in its early stages will always remain crucial [1,2]. Breast ultrasound is widely used in clinical diagnosis for its advantages of safety and low cost. Accurate tumor segmentation is necessary and significant for precise diagnosis using breast ultrasound. Fully automatic segmentation methods are difficult to obtain accurate results that can meet clinical analysis standards [3]. This is mainly related to the poor quality of the ultrasound images, and to the limitations of the segmentation model. In a real clinical situation, each patient may have multiple ultrasound images, and it is unrealistic to use manual annotation of tumor boundaries for all of them. Interactive segmentation tools with fast implementation of high accuracy segmentation have a significant meaning for clinical use

Methods

Results

Conclusion