College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China.
Radiology Department, Affiliated Hospital of Guizhou, Medical Hospital, China.
Biomed Res Int. 2021 Jul 20;2021:9962109. doi: 10.1155/2021/9962109. eCollection 2021.
Early breast cancer detection is one of the most important issues that need to be addressed worldwide as it can help increase the survival rate of patients. Mammograms have been used to detect breast cancer in the early stages; if detected in the early stages, it can drastically reduce treatment costs. The detection of tumours in the breast depends on segmentation techniques. Segmentation plays a significant role in image analysis and includes detection, feature extraction, classification, and treatment. Segmentation helps physicians quantify the volume of tissue in the breast for treatment planning. In this work, we have grouped segmentation methods into three groups: classical segmentation that includes region-, threshold-, and edge-based segmentation; machine learning segmentation; and supervised and unsupervised and deep learning segmentation. The findings of our study revealed that region-based segmentation is frequently used for classical methods, and the most frequently used techniques are region growing. Further, a median filter is a robust tool for removing noise. Moreover, the MIAS database is frequently used in classical segmentation methods. Meanwhile, in machine learning segmentation, unsupervised machine learning methods are more frequently used, and U-Net is frequently used for mammogram image segmentation because it does not require many annotated images compared with other deep learning models. Furthermore, reviewed papers revealed that it is possible to train a deep learning model without performing any preprocessing or postprocessing and also showed that the U-Net model is frequently used for mammogram segmentation. The U-Net model is frequently used because it does not require many annotated images and also because of the presence of high-performance GPU computing, which makes it easy to train networks with more layers. Additionally, we identified mammograms and utilised widely used databases, wherein 3 and 28 are public and private databases, respectively.
早期乳腺癌检测是全球范围内需要解决的最重要问题之一，因为它可以帮助提高患者的生存率。乳腺 X 光检查已被用于早期检测乳腺癌，如果在早期发现，可大大降低治疗成本。乳房肿瘤的检测依赖于分割技术。分割在图像分析中起着重要的作用，包括检测、特征提取、分类和治疗。分割有助于医生对治疗计划中的乳房组织体积进行量化。在这项工作中，我们将分割方法分为三组：包括基于区域、阈值和边缘的分割的经典分割；机器学习分割；以及监督和无监督和深度学习分割。我们的研究结果表明，基于区域的分割是经典方法中常用的方法，最常用的技术是区域生长。此外，中值滤波器是一种用于去除噪声的强大工具。此外，MIAS 数据库在经典分割方法中经常被使用。与此同时，在机器学习分割中，无监督机器学习方法更常用，U-Net 常用于乳腺 X 光图像分割，因为与其他深度学习模型相比，它不需要许多注释图像。此外，回顾的文献表明，可以在不进行任何预处理或后处理的情况下训练深度学习模型，并且还表明 U-Net 模型经常用于乳腺 X 光图像分割。U-Net 模型经常被使用，因为它不需要许多注释图像，并且由于存在高性能 GPU 计算，使得更容易训练具有更多层的网络。此外，我们还确定了乳腺 X 光片并利用了广泛使用的数据库，其中 3 个是公共数据库，28 个是私有数据库。
