Macaque neuron instance segmentation only with point annotations based on multiscale fully convolutional regression neural network

In the field of biomedicine, instance segmentation / individualization is important in analyzing the number, the morphology and the distribution of neurons for the whole slide images. Traditionally, biologists apply the stereology technique to manually count the number of neurons in the regions of interest and estimate the number in anatomical regions or the entire brain. This is very tedious and time-consuming. In this paper, we propose a multiscale fully convolutional regression neural network combined with a competitive region growing technique to individualize size-varying and touching neurons in the major anatomical regions of the macaque brain. Given that neuron instance or contour annotations are infeasible to obtain in certain regions, such as the dentate gyrus where thousands of touching neurons are present, we ask an expert to perform point annotations in the center location of neurons (noted as neuron centroids) for training. Thanks to the multiscale resolution achieved by parallel multiple receptive fields and different network depths, our proposed network succeeds in detecting the centroids of size-varying and touching neurons. Competitive region growing is then applied on these centroids to achieve neuron instance segmentation. Experiments on the macaque brain data suggest that our proposed method outperform the state-of-the-art methods in terms of neuron instance segmentation performance. To our knowledge, this is the first deep learning research work to individualize size-varying and touching neurons only using point annotations in major anatomical regions of the macaque brain.

Automated summary

In this study, a method combining multiscale fully convolutional regression neural network and competitive region growing technique successfully achieved individualization of size-varying and touching neurons in major anatomical regions of the macaque brain.