Subtracting-adding strategy for necrotic lesion segmentation in osteonecrosis of the femoral head.

ABSTRACT

PURPOSE: Osteonecrosis of the femoral head (ONFH) is a severe bone disease that can progressively lead to hip dysfunction. Accurately segmenting the necrotic lesion helps in diagnosing and treating ONFH. This paper aims at enhancing deep learning models for necrosis segmentation. METHODS: Necrotic lesions of ONFH are confined to the femoral head. Considering this domain knowledge, we introduce a preprocessing procedure, termed the "subtracting-adding" strategy, which explicitly incorporates this domain knowledge into the downstream deep neural network input. This strategy first removes the voxels outside the predefined volume of interest to "subtract" irrelevant information, and then it concatenates the bone mask with raw data to "add" anatomical structure information. RESULTS: Each of the tested off-the-shelf networks performed better with the help of the "subtracting-adding" strategy. The dice similarity coefficients increased by 10.93%, 9.23%, 9.38% and 1.60% for FCN, HRNet, SegNet and UNet, respectively. The improvements in FCN and HRNet were statistically significant. CONCLUSIONS: The "subtracting-adding" strategy enhances the performance of general-purpose networks in necrotic lesion segmentation. This strategy is compatible with various semantic segmentation networks, alleviating the need to design task-specific models.