Biological Tissue Sections Instance Segmentation Based on Active Learning

Precisely identifying the locations of biological tissue sections on the wafer is the basis for microscopy imaging. However, the sections made of different biological tissues are different in shape. Therefore, the instance segmentation network trained in the existing dataset may not be suitable for detecting new sections, and the cost of making the new dataset is high. Therefore, this paper proposes an active learning algorithm for biological tissue section instance segmentation. The algorithm can achieve better results with only a few images for training when facing the new segmentation task of biological tissue sections. The algorithm adds a loss prediction module on the instance segmentation network, weights the uncertainty of the instance segmentation mask by the posterior category probability, and finally calculates the value of the sample. Then, we select the sample with the most significant value as the training set, so we can only label a small number of samples, and the network can achieve the expected performance. The algorithm is robust to different shapes of tissue sections and can be applied to various complex scenes to segment tissue sections automatically. Furthermore, experiments show that only labeling 30% samples of the whole training set makes the network achieve the expected performance.