A New Window Loss Function for Bone Fracture Detection and Localization in X-ray Images with Point-based Annotation
This work addresses the problem of accurately detecting and localizing bone fractures for medical professionals, particularly when traditional bounding box annotations are unsuitable due to ambiguous lesion boundaries, offering a more effective solution.
This paper introduces a new method for detecting and localizing bone fractures in X-ray images using point-based annotations, which are converted into pixel-wise supervision with lower and upper bounds. The proposed Window Loss function, which only penalizes predictions outside uncertain regions, achieved an AUROC of 0.983 and an FROC score of 89.6% on a dataset of 4410 pelvic X-ray images, outperforming existing baselines.
Object detection methods are widely adopted for computer-aided diagnosis using medical images. Anomalous findings are usually treated as objects that are described by bounding boxes. Yet, many pathological findings, e.g., bone fractures, cannot be clearly defined by bounding boxes, owing to considerable instance, shape and boundary ambiguities. This makes bounding box annotations, and their associated losses, highly ill-suited. In this work, we propose a new bone fracture detection method for X-ray images, based on a labor effective and flexible annotation scheme suitable for abnormal findings with no clear object-level spatial extents or boundaries. Our method employs a simple, intuitive, and informative point-based annotation protocol to mark localized pathology information. To address the uncertainty in the fracture scales annotated via point(s), we convert the annotations into pixel-wise supervision that uses lower and upper bounds with positive, negative, and uncertain regions. A novel Window Loss is subsequently proposed to only penalize the predictions outside of the uncertain regions. Our method has been extensively evaluated on 4410 pelvic X-ray images of unique patients. Experiments demonstrate that our method outperforms previous state-of-the-art image classification and object detection baselines by healthy margins, with an AUROC of 0.983 and FROC score of 89.6%.