FEEDNet: a feature enhanced encoder-decoder LSTM network for nuclei instance segmentation for histopathological diagnosis.Objective.Automated cell nuclei segmentation is vital for the histopathological diagnosis of cancer.  The goal of this paper is to propose a novel deep learning model for accurately segmenting the nuclei in HE-stained WSIs.Approach.We introduce FEEDNet, a novel encoder-decoder network that uses LSTM units and 'feature enhancement blocks' (FE-blocks).  FEEDNet uses an LSTM unit to capture multi-channel representations compactly.  Using this information, we generate more accurate binary masks than that generated by conventional binary segmentation models.Main results.We have thoroughly evaluated FEEDNet on CoNSeP, Kumar, and CPM-17 datasets.  FEEDNet achieves the best value of PQ (panoptic quality) on CoNSeP and CPM-17 datasets and the second best value of PQ on the Kumar dataset.  The 32-bit floating-point version of FEEDNet has a model size of 64.90 MB.  FEEDNet has a smaller model size than the previous nuclei segmentation networks, which makes it suitable for execution on memory-constrained edge devices.  The state-of-the-art predictive performance of FEEDNet makes it the most preferred network.  The source code can be obtained fromhttps://github.com/CandleLabAI/FEEDNet.
FEEDNet