  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.
