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.

[Percutaneous locking plate combined with Sizhi-Xifang for treatment of the distal tibial fracture].

OBJECTIVE: To observe the clinical effect of Sizhi-Xifang in the improvement of postoperative ankle function. METHODS: From Jan. 2006 to Mar. 2008,49 patients were divided randomly into treatment group and control group. The treatment group included 16 males and 8 females with an average age of (41.3 +/- 13.2) years, involving 13 cases of tye A, 9 of type B and 2 of type C based on AO classification. The control group included 15 males and 10 females with an average age of (38.2 +/- 10.9) years, involving 12 cases of type A, 10 of type B, 3 of type C. Minimal invasive percutaneous plate were used in each group. In treatment group 24 cases were treated with Sizhi-Xifang after the incisions were healed. RESULTS: There were no incision infections, flaps necrosis, bone and plate exposure after treatment in two groups. All patients were followed-up for 4 to 16 months with an average of 8.6 months. According to Johner-Wruhs evaluation standard, there were 7 cases in excellent, 12 good, 5 in fair in treatment group and in control group there were 5 cases in excellent, 7 in good, 10 in fair and 3 in poor. The comparison of effect between two groups had significant difference (P < 0.05). CONCLUSION: Sizhi-Xifang is helpful to improve the ankle postoperative function.