# Lesion Segmentation in Ultrasound Using Semi-pixel-wise Cycle Generative Adversarial Nets

## TL;DR

This paper introduces SPCGAN, a semi-pixel-wise cycle GAN model that improves lesion segmentation in breast ultrasound images, achieving higher accuracy than existing methods, especially with limited training data.

## Contribution

The study presents a novel semi-pixel-wise cycle GAN framework that leverages prior knowledge for improved lesion segmentation in ultrasound images, particularly effective with small datasets.

## Key findings

- Achieved a Dice coefficient of 0.92, outperforming FCN and level set methods.
- Significantly improved malignant lesion segmentation with p<0.001.
- Effective with limited training samples, reducing radiologists' annotation burden.

## Abstract

Breast cancer is the most common invasive cancer with the highest cancer occurrence in females. Handheld ultrasound is one of the most efficient ways to identify and diagnose the breast cancer. The area and the shape information of a lesion is very helpful for clinicians to make diagnostic decisions. In this study we propose a new deep-learning scheme, semi-pixel-wise cycle generative adversarial net (SPCGAN) for segmenting the lesion in 2D ultrasound. The method takes the advantage of a fully convolutional neural network (FCN) and a generative adversarial net to segment a lesion by using prior knowledge. We compared the proposed method to a fully connected neural network and the level set segmentation method on a test dataset consisting of 32 malignant lesions and 109 benign lesions. Our proposed method achieved a Dice similarity coefficient (DSC) of 0.92 while FCN and the level set achieved 0.90 and 0.79 respectively. Particularly, for malignant lesions, our method increases the DSC (0.90) of the fully connected neural network to 0.93 significantly (p$<$0.001). The results show that our SPCGAN can obtain robust segmentation results. The framework of SPCGAN is particularly effective when sufficient training samples are not available compared to FCN. Our proposed method may be used to relieve the radiologists' burden for annotation.