Prostate detection and segmentation based on convolutional neural network and topological derivative
Choongsang Cho, Young Han Lee, Sangkeun Lee · 2017
The topological derivative (TD) for shape analysis has been employed in image segmentation, and machine learning schames based on convolutional neural network (CNN) provide the high performance in the image processing. The supervised and unsupervised approaches have different roles and advantages according to their concepts. To maximize the benefits of two approaches, we propose CNN-TD based segmentation approach. A CNN-based segmentation scheme is employed to faithfully consider the characteristics of an object to be segmented in a given image, and we refine the CNN results using a TD-based scheme. Experimental results show that the proposed scheme produces better performance for the prostate segmentation than the refined results by level set-based schemes. Therefore, we believe that the proposed scheme can be a useful tool for effective medical image segmentation.