Contrast-enhanced magnetic resonance image segmentation based on improved U-Net and Inception-ResNet in the diagnosis of spinal metastases.

Objective: The objective of this study was to investigate the use of contrast-enhanced magnetic resonance imaging (CE-MRI) combined with radiomics and deep learning technology for the identification of spinal metastases and primary malignant spinal bone tumor. Methods: The region growing algorithm was utilized to segment the lesions, and two parameters were defined based on the region of interest (ROI). Deep learning algorithms were employed: improved U-Net, which utilized CE-MRI parameter maps as input, and used 10 layers of CE images as input. Inception-ResNet model was used to extract relevant features for disease identification and construct a diagnosis classifier. Results: The diagnostic accuracy of radiomics was 0.74, while the average diagnostic accuracy of improved U-Net was 0.98, respectively. the PA of our model is as high as 98.001%. The findings indicate that CE-MRI based radiomics and deep learning have the potential to assist in the differential diagnosis of spinal metastases and primary malignant spinal bone tumor. Conclusion: CE-MRI combined with radiomics and deep learning technology can potentially assist in the differential diagnosis of spinal metastases and primary malignant spinal bone tumor, providing a promising approach for clinical diagnosis.

Automated meniscus segmentation and tear detection of knee MRI with a 3D mask-RCNN.

BACKGROUND: The diagnostic results of magnetic resonance imaging (MRI) are essential references for arthroscopy as an invasive procedure. A deviation between medical imaging diagnosis and arthroscopy results may cause irreversible damage to patients and lead to excessive medical treatment. To improve the accurate diagnosis of meniscus injury, it is urgent to develop auxiliary diagnosis algorithms to improve the accuracy of radiological diagnosis. PURPOSE: This study aims to present a fully automatic 3D deep convolutional neural network (DCNN) for meniscus segmentation and detects arthroscopically proven meniscus tears. MATERIALS AND METHODS: Our institution retrospectively included 533 patients with 546 knees who underwent knee magnetic resonance imaging (MRI) and knee arthroscopy. Sagittal proton density-weighted (PDW) images in MRI of 382 knees were regarded as a training set to train our 3D-Mask RCNN. The remaining data from 164 knees were used to validate the trained network as a test set. The masks were hand-drawn by an experienced radiologist, and the reference standard is arthroscopic surgical reports. The performance statistics included Dice accuracy, sensitivity, specificity, FROC, receiver operating characteristic (ROC) curve analysis, and bootstrap test statistics. The segmentation performance was compared with a 3D-Unet, and the detection performance was compared with radiological evaluation by two experienced musculoskeletal radiologists without knowledge of the arthroscopic surgical diagnosis. RESULTS: Our model produced strong Dice coefficients for sagittal PDW of 0.924, 0.95 sensitivity with 0.823 FPs/knee. 3D-Unet produced a Dice coefficient for sagittal PDW of 0.891, 0.95 sensitivity with 1.355 FPs/knee. The difference in the areas under 3D-Mask-RCNN FROC and 3D-Unet FROC was statistically significant (p = 0.0011) by bootstrap test. Our model detection performance achieved an area under the curve (AUC) value, accuracy, and sensitivity of 0.907, 0.924, 0.941, and 0.785, respectively. Based on the radiological evaluations, the AUC value, accuracy, sensitivity, and specificity were 0.834, 0.835, 0.889, and 0.754, respectively. The difference in the areas between 3D-Mask-RCNN ROC and radiological evaluation ROC was statistically significant (p = 0.0009) by bootstrap test. 3D Mask RCNN significantly outperformed the 3D-Unet and radiological evaluation demonstrated by these results. CONCLUSIONS: 3D-Mask RCNN has demonstrated efficacy and precision for meniscus segmentation and tear detection in knee MRI, which can assist radiologists in improving the accuracy and efficiency of diagnosis. It can also provide effective diagnostic indicators for orthopedic surgeons before arthroscopic surgery and further promote precise treatment.

Treatment of irreducible femoral intertrochanteric fractures using a wire-guided device.

PURPOSE: Treatment of irreducible femoral intertrochanteric fractures often requires open reduction. However, the technique unavoidably causes patients to suffer greater trauma. As such, minimally invasive techniques should be employed to reduce the surgical-related trauma on these patients and maintain a stable reduction of the fractures. Herein, a minimally invasive wire introducer was designed and used for the treatment of femoral intertrochanteric fractures. The effectiveness of using a wire-guided device to treat irreducible femoral intertrochanteric fractures was evaluated. METHODS: Between 2013 and 2018, patients with femoral intertrochanteric fractures who were initially treated by intramedullary nail fixation but had difficult reduction using the traction beds were retrospectively reviewed. Decision for an additional surgery was based on the displacement of the fracture. The patients were then divided into two groups: those in the control group received an open reduction surgery while those in the observation group received a closed reduction surgery using a minimally invasive wire introducer to guide the wire that could assist in fracture reduction. The operation time, blood loss, visual analogue scale scores, angulation, reduction, neck-shaft angle, re-displacement, limb length discrepancy, and union time were then recorded and analyzed to determine the efficiency of the wire introducer technique. Categorical variables were analyzed by using Chi-square test, while continuous variables by independent t-test and the Mann-Whitney test accordingly. RESULTS: There were 92 patients included in this study: 61 in the control group and 31 in the observation group. There were no significant differences in baseline demographic factors between the two groups. All surgeries were successful with no deaths within the perioperative period. The average follow-up time for the patients was 23.8 months. However, the observation group had a significantly shorter operation time, lower visual analogue scale score, less intraoperative bleeding, and shorter fracture healing time. There were no significant differences in the angulation, reduction, neck-shaft angle, and limb length discrepancy between the two groups. CONCLUSION: The minimally invasive wire guide achieved a similar effect to that of open reduction in the treatment of intertrochanteric fractures with difficult reduction. Moreover, the minimally invasive wire introducer is a good technology that accurately guides the wire during reduction. Indeed, it is an effective technique and achieves good clinical outcomes in restoration of irreducible femoral intertrochanteric fractures.