Deep 3D Convolutional Networks to Segment Bones Affected by Severe Osteoarthritis in CT Scans for PSI-Based Knee Surgical Planning

• Main Authors: Davide Marzorati, Mattia Sarti, Luca Mainardi, Alfonso Manzotti, Pietro Cerveri
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: 3D U-net; bone segmentation; CT~images; deep learning; knee arthroplasty; osteoarthritis
• Online Access: https://ieeexplore.ieee.org/document/9241701/

Segmentation of bony structures in CT scans is a crucial step in knee arthroplasty based on personalized surgical instruments (PSI). As a matter of fact, the success of the surgery depends on the quality of the matching between the patient-specific resection jigs, manufactured exploiting the patient bony surfaces attained by segmentation, and true patient surfaces. Severe pathological conditions as chronic osteoarthritis, deteriorating the cartilages, narrowing the intra-articular spaces and leading to bone impingement, complicate the segmentation making the recognition of bony boundaries sub-optimal for traditional semi-automated methods and often extremely difficult even for expert radiologists. Deep convolutional neural networks (CNNs) have been investigated in the last years towards automatic labeling of diagnostic images, especially harnessing the encoding-decoding U-Net architecture. In this article, we implemented deep CNNs to encompass the concurrent segmentation of the distal femur and the proximal tibia in CT images and evaluate how segmentation uncertainty may impact on the surgical planning. A retrospective set of 200 knee CT scans of patients was used to train the network and test the segmentation performances. Tests on a subset of 20 scans provided median dice, sensitivity and positive predictive value indices greater than 96% for both shapes, with median 3D reconstruction error in the range of 0.5mm. Median 3D errors on both PSI femoral and tibial contact areas and surgical cut alignments were less than 2mm and 2°, respectively, which can be considered clinically acceptable. These results substantiate that deep CNN architectures can disclose the opportunity of segmenting bone shapes in CT scans for PSI-based surgical planning with promising accuracy. However, we observed that segmentation scores alone cannot be taken as representative of the 3D errors at the contact areas of the PSI. Therefore when comparing segmentation algorithms of PSI-based surgical planning the 3D errors should be explicitly analyzed.