Transfer Learning-Based Neuronal Cell Instance Segmentation With Pointwise Attentive Path Fusion

Accurate instance segmentation is essential for the behavior and morphology analysis of neuronal cells. The main challenges of this segmentation task involve irregular and concave cell morphology, low contrast on cell boundaries, cell clustering and adhesion, and the background noise in the phase contrast microscopy (PCM) images. To address these challenges, we propose a learning pipeline with three performance boosters that have not been extensively explored in prior studies, including transferring knowledge from a model pre-trained on a larger but similar dataset, enhancing the contrast of cells in a PCM image, and a pointwise attentive path fusion module that allows the learning model to capture informative features from critical areas. Experiments have been conducted on the Sartorius Cell Instance Segmentation dataset with three neuronal cell lines. Results show that the final model, with three boosters enabled, brings an mAP gain of 10.3%. Compared to the top three places on the leaderboard, our method shows comparable performance without using any ensemble method, making our model the state-of-the-art solution among the single model-based methods.