Breast ultrasound image segmentation: A coarse‐to‐fine fusion convolutional neural network. Issue 8 (29th July 2021)

• Record Type: Journal Article
• Authors: Wang, Ke; Liang, Shujun; Zhong, Shengzhou; Feng, Qianjin; Ning, Zhenyuan; Zhang, Yu
• Is Part Of: Medical physics. Volume 48:Issue 8(2021)
• Page Start: 4262
• Page End: 4278
• Publication Date: 2021-07-29
• Subjects: breast ultrasound image segmentation -- coarse‐to‐fine fusion -- convolutional neural network -- deep learning; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15006
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Breast ultrasound (BUS) image segmentation plays a crucial role in computer‐aided diagnosis systems for BUS examination, which are useful for improved accuracy of breast cancer diagnosis. However, such performance remains a challenging task owing to the poor image quality and large variations in the sizes, shapes, and locations of breast lesions. In this paper, we propose a new convolutional neural network with coarse‐to‐fine feature fusion to address the aforementioned challenges. Methods: The proposed fusion network consists of an encoder path, a decoder path, and a core fusion stream path (FSP). The encoder path is used to capture the context information, and the decoder path is used for localization prediction. The FSP is designed to generate beneficial aggregate feature representations (i.e., various‐sized lesion features, aggregated coarse‐to‐fine information, and high‐resolution edge characteristics) from the encoder and decoder paths, which are eventually used for accurate breast lesion segmentation. To better retain the boundary information and alleviate the effect of image noise, we input the superpixel image along with the original image to the fusion network. Furthermore, a weighted‐balanced loss function was designed to address the problem of lesion regions having different sizes. We then conducted exhaustive experiments on three public BUS datasets to evaluate the proposed network. Results: The proposed method outperformed state‐of‐the‐art (SOTA) segmentation methods on the three public BUS datasets, with average dice similarity coefficients of 84.71(±1.07), 83.76(±0.83), and 86.52(±1.52), average intersection‐over‐union values of 76.34(±1.50), 75.70(±0.98), and 77.86(±2.07), average sensitivities of 86.66(±1.82), 85.21(±1.98), and 87.21(±2.51), average specificities of 97.92(±0.46), 98.57(±0.19), and 99.42(±0.21), and average accuracies of 95.89(±0.57), 97.17(±0.3), and 98.51(±0.3). Conclusions: The proposed fusion network could effectively segment lesions from BUS images, thereby presenting a new feature fusion strategy to handle challenging task of segmentation, while outperforming the SOTA segmentation methods. The code is publicly available at https://github.com/mniwk/CF2‐NET .