Liver segmentation in CT images using three dimensional to two dimensional fully convolutional network
This addresses the need for faster and more accurate liver segmentation in CT scans for radiologists, particularly in emergency situations, though it appears incremental.
The paper tackled liver segmentation in CT images by proposing a 3D to 2D fully convolutional network with conditional random field enhancement, achieving a Dice score of 93.52 in under a minute.
The need for CT scan analysis is growing for pre-diagnosis and therapy of abdominal organs. Automatic organ segmentation of abdominal CT scan can help radiologists analyze the scans faster and segment organ images with fewer errors. However, existing methods are not efficient enough to perform the segmentation process for victims of accidents and emergencies situations. In this paper we propose an efficient liver segmentation with our 3D to 2D fully connected network (3D-2D-FCN). The segmented mask is enhanced by means of conditional random field on the organ's border. Consequently, we segment a target liver in less than a minute with Dice score of 93.52.