Upper and lower extremity bone segmentation with Mask R-CNN

Abstract

Most medical image processing studies use medical images to detect and measure the structure of organs and bones. The segmentation of image data is of great importance for the determination of the area to be studied and for the reduction of the size of the data to be studied. Working with image data creates an exponentially increasing workload depending on the size and number of images and requires high computing power using machine learning methods. Our study aims to achieve high success in bone segmentation, the first step in medical object detection studies. In many situations and cases, such as fractures and age estimation, the humerus and radius of the upper extremity and the femur and tibia of the lower extremity of the human skeleton provide data. In our bone segmentation study on X-RAY images, 160 images from one hundred patients were collected using data compiled from accessible databases. A segmentation result with an average accuracy of 0.981 was obtained using the Mask R-CNN method with the resnet50 architecture.