A hybrid computational pathology method for the detection of perineural invasion junctions
Abstract: 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.
Loading