A deep learning approach for automatic 3D segmentation of hip cartilage and labrum from direct hip MR arthrography.

The objective was to use convolutional neural networks (CNNs) for automatic segmentation of hip cartilage and labrum based on 3D MRI. In this retrospective single-center study, CNNs with a U-Net architecture were used to develop a fully automated segmentation model for hip cartilage and labrum from MRI. Direct hip MR arthrographies (01/2020-10/2021) were selected from 100 symptomatic patients.

Deep-Learning-Based Segmentation of the Shoulder from MRI with Inference Accuracy Prediction.

Three-dimensional (3D)-image-based anatomical analysis of rotator cuff tear patients has been proposed as a way to improve repair prognosis analysis to reduce the incidence of postoperative retear. However, for application in clinics, an efficient and robust method for the segmentation of anatomy from MRI is required. We present the use of a deep learning network for automatic segmentation of the humerus, scapula, and rotator cuff muscles with integrated automatic result verification.

Does the dGEMRIC Index Recover 3 Years After Surgical FAI Correction and an Initial dGEMRIC Decrease at 1-Year Follow-up? A Controlled Prospective Study.

Background: Delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC) allows objective and noninvasive assessment of cartilage quality. An interim analysis 1 year after correction of femoroacetabular impingement (FAI) previously showed that the dGEMRIC index decreased despite good clinical outcome.

Purpose: To evaluate dGEMRIC indices longitudinally in patients who underwent FAI correction and in a control group undergoing nonoperative treatment for FAI.

A Deep Learning Method for Quantification of Femoral Head Necrosis Based on Routine Hip MRI for Improved Surgical Decision Making.

(1) Background: To evaluate the performance of a deep learning model to automatically segment femoral head necrosis (FHN) based on a standard 2D MRI sequence compared to manual segmentations for 3D quantification of FHN. (2) Methods: Twenty-six patients (thirty hips) with avascular necrosis underwent preoperative MR arthrography including a coronal 2D PD-w sequence and a 3D T1 VIBE sequence. Manual ground truth segmentations of the necrotic and unaffected bone were then performed by an expert reader to train a self-configuring nnU-Net model.

Automated quantification of cartilage quality for hip treatment decision support.

Purpose: Preservation surgery can halt the progress of joint degradation, preserving the life of the hip; however, outcome depends on the existing cartilage quality. Biochemical analysis of the hip cartilage utilizing MRI sequences such as delayed gadolinium-enhanced MRI of cartilage (dGEMRIC), in addition to morphological analysis, can be used to detect early signs of cartilage degradation. However, a complete, accurate 3D analysis of the cartilage regions and layers is currently not possible due to a lack of diagnostic tools.