Rethinking Computer-Aided Pelvis Segmentation

As an important structure connecting the spine and lower limbs, the abnormal pelvis is one of the threats to human health worldwide, leading to millions of deaths every year. Although early diagnosis and treatment can greatly improve the chances of survival, it remains a major challenge, especially for pelvis fractures. Computer-aided pelvis segmentation (CPS) is a promising choice for the abnormal pelvis due to the great success of deep learning. However, when it comes to the abnormal pelvis diagnosis, the lack of training data has hampered the progress of CPS. To solve this problem, we have published a large-scale pelvis dataset, namely the Pelvis Computed tomography (CT) image (PCT14K) dataset. This dataset contains 14487 CT slices with the corresponding label for pelvis areas, while the existing largest public pelvis dataset part comes from existing data sets related to other organs with a lot of redundant information, and another part comes from the orthopedic hospital without a corresponding label. The proposed dataset enables the training of sophisticated segmentation networks for high-quality CPS. Some mainstream segmentation algorithms are trained and evaluated on the proposed PCT14K dataset and served as the baselines for future research. The published dataset will be available at https://github.com/YUAN-SIMING/PCT14K.