Breast Cancer Segmentation Methods: Current Status and Future Potentials

Abstract

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.

Deep learning-based automatic segmentation for size and volumetric measurement of breast cancer on magnetic resonance imaging

“…Segmentation plays a significant role in image analysis, including detection, feature extraction, classification, and treatment ( 21 , 22 ). Automatic and semiautomatic segmentation can alleviate the labor-intensive problems and eliminate the high variability between intra- and inter-observers ( 23 ). Moreover, deep learning, as a subset of AI, is a promising method to make a tremendous progress in automatic segmentation by which more reproducible and effective texture features in different fields of image analysis are extracted ( 24 – 26 ).…”

Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder

“…al. [ 25 ] for all Breast Cancer Segmentation Methods. In their research, they review the deep learning segmentation methods that are used to extract masses from mammogram images and highlight the most frequently used of them.…”

Multiclass Semantic Segmentation of Immunostained Breast Cancer Tissue with a Deep-Learning Approach

“…Deep learning methods have reported outstanding results and are mainly based on different neural networks which need to be trained to determine certain network output (classification or segmentation). U-Net is a network frequently used for segmentation task [16]. However its performance depends on the variance of the several visual patterns that might be present in the images, and the amount of images available for robust learning of those patterns during a training process.…”

“…Image segmentation of histopathology images is a key task and widely used for tumour detection [16]. Nuclei and cell Segmentation of histopathology images are basic steps required in histopathology analysis, as well as feature extraction and classification [6, 12, 26].…”