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Coordinate-guided U-Net for automated breast segmentation on MRI images
Author(s): Xinpeng Zheng; Zhuangsheng Liu; Lin Chang; Wansheng Long; Yao Lu
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Paper Abstract

Magnetic resonance imaging (MRI) plays an important role in breast cancer detection and diagnosis. Breast region segmentation on MRI images is an essential step for many analysis tasks such as the assessment of background parenchymal enhancement (BPE), the analysis of non-mass enhancement (NME) and investigation of the tumor characteristics. However, automated breast segmentation remains a challenge because the noisy background and the blurred boundary between breast and pectoral muscle on MRI images. In this study, we developed a coordinate-guided U-Net (CU-Net) to identify the boundary of the breast on MRI images. The CU-Net applied the breast position information to the segmentation of breast by adding the X coordinate channel and Y coordinate channel to the input image. A dataset of 80 3D bilateral breast MRI scans including 64 cases in training/validation set and 16 cases in test set were collected. All the input images were resized to 256×256 pixels and the intensity are cropped and rescaled according to the percentile of cumulative histogram to normalize the image. To add X and Y coordinate channels, the middle point of the breast-air boundary was detected as the origin of coordinates. The position of each pixel in the coordinates was then encoded into the input of the network. The CU-Net was evaluated with three measures: the dice coefficient (Dice), the intersection over union (IoU) and the hausdorff distance (Hdist). The results of CU-Net were compared to those obtained with U-Net method. In the test set including 16 cases, the mean Dice, the mean IoU, and the mean Hdist of CUNet were 94.1±1.1%, 92.5±1.3%, and 6.17±1.4 mm, while the corresponding measures by U-Net were 91.4±2.4%, 89.7±1.7%, and 6.98±1.5 mm, respectively. The results showed that our CU-Net was able to segment breast on MRI images reliably and was more accurate comparing to the U-Net.

Paper Details

Date Published: 6 May 2019
PDF: 6 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 1106928 (6 May 2019); doi: 10.1117/12.2524250
Show Author Affiliations
Xinpeng Zheng, Sun Yat-Sen Univ. (China)
Zhuangsheng Liu, Jiangmen Central Hospital (China)
Lin Chang, Children’s Hospital of Nanjing Medical Univ. (China)
Wansheng Long, Jiangmen Central Hospital (China)
Yao Lu, Sun Yat-Sen Univ. (China)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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