Can deep learning reduce the time and effort required for manual segmentation in 3D reconstruction of MRI in rotator...

The use of MRI as a diagnostic tool has gained popularity in the field of orthopedics. Although 3-dimensional (3D) MRI offers more intuitive visualization and can better facilitate treatment planning than 2-dimensional (2D) MRI, manual segmentation for 3D visualization is time-consuming and lacks reproducibility. Recent advancements in deep learning may provide a solution to this problem through the process of automatic segmentation. The purpose of this study was to develop automated semantic segmentation on 2D MRI images of rotator cuff tears by using a convolutional neural network to visualize 3D models of related anatomic structures.

MRI scans from 56 patients with rotator cuff tears (T2 Linear Coronal MRI; 3.0T, 512 mm × 512 mm, and 2.5-mm slice thickness) were collected. Segmentation masks for the cuff tendon, muscle, bone, and cartilage were obtained by four orthopedic shoulder surgeons, and these data were revised by a shoulder surgeon with more than 20 years' experience. We performed 2D and 3D segmentation using nnU-Net with secondary labels for reducing false positives. Final validation was performed in an external T2 MRI dataset (10 cases) acquired from other institutions. The Dice Similarity Coefficient (DSC) was used to validate segmentation quality.

The use of 3D nnU-Net with secondary labels to reduce false positives achieved satisfactory results, even with a limited amount of data. The DSCs (mean ± SD) of the cuff tendon, muscle, bone, and cartilage in the internal test set were 80.7% ± 9.7%, 85.8% ± 8.6%, 97.8% ± 0.6%, and 80.8% ± 15.1%, respectively. In external validation, the DSC of the tendon segmentation was 82.74±5.2%.

Automated segmentation using 3D U-Net produced acceptable accuracy and reproducibility. This method could provide rapid, intuitive visualization that can significantly facilitate the diagnosis and treatment planning in patients with rotator cuff tears.