  Nuclear segmentation in histology images is a challenging task due to
significant variations in the shape and appearance of nuclei. One of the main
hurdles in nuclear instance segmentation is overlapping nuclei where a smart
algorithm is needed to separate each nucleus. In this paper, we introduce a
proposal-free deep learning based framework to address these challenges. To
this end, we propose a spatially-aware network (SpaNet) to capture spatial
information in a multi-scale manner. A dual-head variation of the SpaNet is
first utilized to predict the pixel-wise segmentation and centroid detection
maps of nuclei. Based on these outputs, a single-head SpaNet predicts the
positional information related to each nucleus instance. Spectral clustering
method is applied on the output of the last SpaNet, which utilizes the nuclear
mask and the Gaussian-like detection map for determining the connected
components and associated cluster identifiers, respectively. The output of the
clustering method is the final nuclear instance segmentation mask. We applied
our method on a publicly available multi-organ data set and achieved
state-of-the-art performance for nuclear segmentation.
