Few-Shot Learning For Auromatic Intracranial Hematoma Segmentation

Intracranial hemorrhage is a serious craniocerebral injury. The automatic segmentation of intracranial hematoma is of great significance for the diagnosis of patients with intracranial hemorrhage. So far, image segmentation methods based on deep learning have made great progress. However, the accuracy and efficiency of hematoma segmentation still can not meet the requirements of clinical application due to the particularity of brain hematoma images, such as sparse samples, uneven sample distribution, diverse hematoma morphology and inconsistent scale. In order to solve these problems, this paper proposes a FewShot segmentation framework based on confrontation. The framework abandons the solution process based on fixed loss function in the existing segmentation model, and realizes confrontation learning through the confrontation between the discrimination model and the segmentation model. It not only solves the problem that hematoma samples are scarce and difficult to label, but also adopts the way of confrontation learning, so that the model pays more attention to the overall characteristics of hematoma rather than local information in the segmentation process, So as to make it more universal. At the same time, this paper designs a multi-scale convolution algorithm, which uses four different scale convolution cores to realize feature convolution and spatial fusion, so that the model can maintain a certain accuracy range when segmenting hematomas of different shapes and sizes, and solves the problem of multi-scale hematoma segmentation. In order to speed up the operation of the segmentation model, a mask is added to the image segmentation model for the first time to replace the reconstruction process from feature to segmented image, which greatly saves the computational cost. In the experiment, the open data set cq500 is used to develop the algorithm, and the image data provided by the First Affiliated Hospital of Chongqing Medical University are used to test. The results show that compared with the most advanced model, this method has the highest comprehensive evaluation index and significantly improves the segmentation efficiency.