Transforming Auto-Encoder and Decoder Network for Pediatric Bone Image Segmentation using a State-of-the-art Semantic Segmentation network on Bone Radiographs

Semantic Image segmentation is one of the toughest problems in computer vision. It is a task that requires a vision system, that can capture the pose and the location of an option to a high degree of accuracy. The typical deep learning-based solutions for automatic image segmentation use Max Pooling layers as part of the vision system which causes the system to lose the property of equivariance. In this paper, we use the state-of-the-art transforming auto-encoder and decoder network, which is known for being equivariant, to segment pediatric bone radiographs. The dataset used consists of about 12600 images. Contrast Limited Adaptive Histogram Equalization is applied to all images before feeding them as input to the trained transforming auto-encoder. Following this, morphological operations are performed to fill the holes in the output and also draw the contours of image and generate the final mask. The result is also compared with those fetched from some of the extant highly popular medical image segmentation vision system. To our knowledge, this is the first paper that utilizes transforming auto-encoders for the purpose of Pediatric bone image segmentation.