Region-based Convolution Neural Network Approach for Accurate Segmentation of Pelvic Radiograph
This work addresses the need for reliable segmentation in medical imaging for surgical pre-planning, representing an incremental improvement over existing methods.
The paper tackles the problem of segmenting pelvic bones in radiograph images, which is crucial for applications like automatic pose estimation and disease detection, by proposing a novel multi-task segmentation method based on Mask R-CNN with transfer learning and data augmentation, achieving a DICE coefficient of 0.96 that significantly outperforms U-Net.
With the increasing usage of radiograph images as a most common medical imaging system for diagnosis, treatment planning, and clinical studies, it is increasingly becoming a vital factor to use machine learning-based systems to provide reliable information for surgical pre-planning. Segmentation of pelvic bone in radiograph images is a critical preprocessing step for some applications such as automatic pose estimation and disease detection. However, the encoder-decoder style network known as U-Net has demonstrated limited results due to the challenging complexity of the pelvic shapes, especially in severe patients. In this paper, we propose a novel multi-task segmentation method based on Mask R-CNN architecture. For training, the network weights were initialized by large non-medical dataset and fine-tuned with radiograph images. Furthermore, in the training process, augmented data was generated to improve network performance. Our experiments show that Mask R-CNN utilizing multi-task learning, transfer learning, and data augmentation techniques achieve 0.96 DICE coefficient, which significantly outperforms the U-Net. Notably, for a fair comparison, the same transfer learning and data augmentation techniques have been used for U-net training.