Regionally Adaptive Active Learning Framework for Nuclear Segmentation in Microscopy Image

Recent innovations in tissue clearing and light-sheet microscopy allow the rapid acquisition of intact micron-resolution images in fluorescently labeled samples. Automated, accurate, and high-throughput nuclear segmentation methods are in high demand to quantify the number of cells and evaluate cell-type specific marker co-labeling. Complete quantification of cellular level differences in genetically manipulated animal models will allow localization of organ structural differences well beyond what has previously been accomplished through slice histology or MRI. This paper proposes a nuclei identification tool for accurate nuclear segmentation from tissue-cleared microscopy images by regionally adaptive active learning. We gradually improved high-level nuclei-to-nuclei contextual heuristics to determine a non-linear mapping from local image appearance to the segmentation label at the center of each local neighborhood. In addition, we propose an adaptive fine-tuning (FT) strategy to tackle the complex segmentation task of separating nuclei in close proximity, allowing for the precise quantification of structures where nuclei are often densely packed. Compared to the current nuclei segmentation methods, we have achieved more accurate and robust nuclear segmentation results in various complex scenarios.