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首页> 外文会议>International Workshop on Breast Imaging >Deep learning-based segmentation of mammary gland region in digital mammograms of scattered mammary glands and fatty breasts
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Deep learning-based segmentation of mammary gland region in digital mammograms of scattered mammary glands and fatty breasts

机译：在分散的乳腺和肥大乳房的数字乳腺X线照片中基于深度学习的乳腺区域分割

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This study is aimed to automatically segment mammary gland region into scattered mammary glands and fatty breasts using deep learning method. Total 433 mediolateral oblique-view mammograms of Japanese women were collected and confirmed for scattered mammary glands or fatty breasts; using BI-RADS's classification. First, manually contoured mammary gland regions were determined for all mammograms as ground truths by three certified radiological technologists. Second, the U-net model was employed to segment the mammary gland region automatically. This model is a type of convolutional neural network (CNN) mainly aimed at medical image segmentation. The segmentation accuracies were assessed based on five criteria. Dice coefficients, breast densities, mean gray values, centroids, and sizes of mammary gland region. The Dice coefficient was 0.915. The mean size of mammary gland regions obtained by the U-net was 8.7% larger than that of the ground truths. The mean centroid coordinates of mammary gland regions by the U-net were shifted 1.6 and 5.4 mm on average in mediolateral and craniocaudal directions, respectively from ground truths. The mean gray value of mammary gland regions obtained by the U-net was only 0.4% higher compared with ground truths. The resultant difference was 0.4% on average in breast densities between ground truths and the segmented mammary gland regions. We found significant similarity in the ground truths and the data generated by deep learning on all the parameters, thereby attesting the efficacy of this method for segmenting the mammary gland regions of not only the dense breasts but also the scattered mammary gland- and fatty- breasts.
机译：这项研究旨在使用深度学习方法将乳腺区域自动分割成分散的乳腺和肥大的乳房。总共收集了433例日本女性的中外侧斜视X线照片，并确认其有散在的乳腺或肥大的乳房。使用BI-RADS的分类。首先，由三名经认证的放射线技术人员确定所有乳房X光照片的人工轮廓乳腺区域作为地面真相。其次，采用U-net模型自动分割乳腺区域。该模型是一种卷积神经网络（CNN），主要用于医学图像分割。基于五个标准评估了细分的准确性。骰子系数，乳房密度，平均灰度值，质心和乳腺区域的大小。骰子系数为0.915。通过U-net获得的乳腺区域的平均大小比基本情况大8.7％。通过U-网，乳腺区域的平均质心坐标分别从地面真实情况向中外侧和颅尾方向平均移动了1.6和5.4 mm。通过U-net获得的乳腺区域的平均灰度值仅比地面真相高0.4％。这样，地面真相和乳腺分割区域之间的乳房密度平均差异为0.4％。我们发现基本事实和深度学习在所有参数上产生的数据具有显着相似性，从而证明了该方法不仅可以分割致密乳房的乳腺区域，而且可以对散布的乳腺和肥大乳房的乳腺区域进行分割。

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