Perineural invasion refers to a process where tumor cells invade, surround, or pass through nerve cells, serving as an indicator of aggressive tumor and related to poor prognosis. Herein, we propose an efficient and effective hybrid computational method for an automated detection of perineural invasion junctions in multi-tissue digitized histology images. The proposed approach conducts the detection of perineural invasion junctions in three stages. The first state identifies candidate regions for perineural invasion. The second stage delineates perineural invasion junctions. The last stage eliminates any false positive regions for perineural invasion. In the first two stages, we exploit an advanced deep neural network. In the last stage, we utilize hand-crafted features and a conventional machine learning algorithm. To evaluate the proposed approach, we employ 150 whole slide images obtained from PAIP2021 Challenge: Perineural Invasion in Multiple Organ Cancer and conduct a five-fold cross-validation. The experimental results show that the proposed hybrid approach could facilitate an automated, accurate identification of perineural invasion in histology images.
In digital pathology, nuclei segmentation still remains a challenging task due to the high heterogeneity and variability in the characteristics of nuclei, in particular, the clustered and overlapping nuclei. We propose a distance ordinal regression loss for an improved nuclei instance segmentation in digitized tissue specimen images. A convolutional neural network with two decoder branches is built. The first decoder branch conducts the nuclear pixel prediction and the second branch predicts the distance to the nuclear center, which is utilized to identify the nuclear boundary and to separate out overlapping nuclei. Adopting a distance-decreasing discretization strategy, we recast the problem of the distance prediction as an ordinal regression problem. To evaluate the proposed method, we conduct experiments on multiple independent multitissue histology image datasets. The experimental results on the multi-tissue datasets demonstrate the effectiveness of the proposed model.
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