A U-net and KMeans based method for brain tumor segmentation and measurement
Hanxu Hu, Zhongliang Guo · 2021
Medical image segmentation is an essential task in the field of image recognition, and has a high requirement for accuracy. As a popular model, although U-net has accomplished many achievements, such as locating localizing the lesion area and object segmentation, it has shortcomings on segmenting quite small details. In this paper, we improve the U-net algorithm by adding ResBlock and Batch Normalization modules, as well as increasing the network depth, so as to improve its accuracy for tumor site segmentation in medical images of the brain. The K-Means algorithm is also used to segment the brain region and the background region to find out the relative size of the brain occupied by the tumor, which achieves both image segmentation and tumor size measurement.