CSR-Net: Cross-Scale Residual Network for multi-objective scaphoid fracture segmentation.

The scaphoid is located in the carpals. Owing to the body structure and location of the scaphoid, scaphoid fractures are common and it is difficult to heal. Three-dimensional reconstruction of scaphoid fracture can accurately display the fracture surface and provide important support for the surgical plan involving screw placement. To achieve this goal, in this study, the cross-scale residual network (CSR-Net) is proposed for scaphoid fracture segmentation. In the CSR-Net, the features of different layers are used to achieve fusion through cross-scale residual connection, which realizes scale and channel conversions between the features of different layers. It can establish close connections between different scale features. The structures of the output layer and channel are designed to establish the CSR-Net as a multi-objective architecture, which can realize scaphoid fracture and hand bone segmentations synchronously. In this study, 65 computed tomography images of scaphoid fracture are tested. Quantitative metrics are used for assessment, and the results obtained show that the CSR-Net achieves higher performance in hand bone and scaphoid fracture segmentations. In the visually detailed display, the fracture surface is clearer and more intuitive than those obtained from other methods. Therefore, the CSR-Net can achieve accurate and rapid scaphoid fracture segmentation. Its multi-objective design provides not only an accurate digital model, but also a prerequisite for navigation in the hand bone.

Cerebrovascular segmentation from TOF-MRA based on multiple-U-net with focal loss function.

BACKGROUND AND OBJECTIVE: Accurate cerebrovascular segmentation plays an important role in the diagnosis of cerebrovascular diseases. Considering the complexity and uncertainty of doctors' manual segmentation of cerebral vessels, this paper proposed an automatic segmentation algorithm based on Multiple-U-net (M-U-net) to segment cerebral vessel structures from the Time-of-flight Magnetic Resonance Angiography (TOF-MRA) data. METHODS: First, the TOF-MRA data was normalized by volume and then divided into three groups through slices of axial, coronal and sagittal directions respectively. Three single U-nets were trained by divided dataset. To solve the problem of uneven distribution of positive and negative samples, the focal loss function was adopted in training. After obtaining the prediction results of three single U-nets, the voting feature fusion and the post-processing process based on connected domain analysis would be performed. 95 volumes of TOF-MRA provided by the MIDAS platform were applied to the experiment, among which 20 volumes were treated as the training dataset, 5 volumes were used as the validation dataset and the remaining 70 volumes were divided into 10 groups to test the trained model respectively. RESULTS: Experiments showed that the proposed M-U-net based algorithm achieved 88.60% and 87.93% Dice Similarity Coefficient (DSC) on the verification dataset and testing dataset, which performed better than any single U-net. CONCLUSIONS: Compared with other existing algorithms, our algorithm reached the state of the art level. The feature fusion of three single U-nets could effectively complement the segmentation results.