CEL-Unet: Distance Weighted Maps and Multi-Scale Pyramidal Edge Extraction for Accurate Osteoarthritic Bone Segmentation in CT Scans

Unet architectures are being investigated for automatic image segmentation of bones in CT scans because of their ability to address size-varying anatomies and pathological deformations. Nonetheless, changes in mineral density, narrowing of joint spaces and formation of largely irregular osteophytes may easily disrupt automatism requiring extensive manual refinement. A novel Unet variant, called CEL-Unet, is presented to boost the segmentation quality of the femur and tibia in the osteoarthritic knee joint. The neural network embeds region-aware and two contour-aware branches in the decoding path. The paper features three main technical novelties: 1) directed connections between contour and region branches progressively at different decoding scales; 2) pyramidal edge extraction in the contour branch to perform multi-resolution edge processing; 3) distance-weighted cross-entropy loss function to increase delineation quality at the sharp edges of the shapes. A set of 700 knee CT scans was used to train the model and test segmentation performance. Qualitatively CEL-Unet correctly segmented cases where the state-of-the-art architectures failed. Quantitatively, the Jaccard indexes of femur and tibia segmentation were 0.98 and 0.97, with median 3D reconstruction errors less than 0.80 and 0.60 mm, overcoming competitive Unet models. The results were evaluated against knee arthroplasty planning based on personalized surgical instruments (PSI). Excellent agreement with reference data was found for femoral (0.11°) and tibial (0.05°) alignments of the distal and proximal cuts computed on the reconstructed surfaces. The bone segmentation was effective for large pathological deformations and osteophytes, making the techniques potentially usable in PSI-based surgical planning, where the reconstruction accuracy of the bony shapes is one of the main critical factors for the success of the operation