Attention U-net for Cell Instance Segmentation
Qianze Liu, Tong Mo, Yiya Yao, Yufei Zhang · 2023
As a major cause of human death and disability, neurological diseases have profound impacts worldwide, yet there is still a lack of effective research to quantify such diseases. As a fundamental but challenging task in the field of medical imaging, segmenting neuronal cell types measured by microscopic observation images to help assess patient status plays a crucial role in the accurate quantification of the disease. To this end, we propose an instance segmentation network of Improved Attention U-net for segmentation of different types of neuronal cells. Specifically, we introduce adaptive mechanism on the basis of U-net structure to enhance the robustness and discriminability of the model. This measure can capture the most important parts in the field of view to filter out redundant information in the target optimization process. In addition, considering the irregular and unique morphology of neuronal cells, we introduce deformable convolution kernels to adapt to the detection needs of various types of cells and lesions. Experimental results show that the algorithm has excellent performance in cell instance segmentation.