  Hirschsprungs disease (HD) is a birth defect which is diagnosed and managed
by multiple medical specialties such as pediatric gastroenterology, surgery,
radiology, and pathology. HD is characterized by absence of ganglion cells in
the distal intestinal tract with a gradual normalization of ganglion cell
numbers in adjacent upstream bowel, termed as the transition zone (TZ).
Definitive surgical management to remove the abnormal bowel requires accurate
assessment of ganglion cell density in histological sections from the TZ, which
is difficult, time-consuming and prone to operator error. We present an
automated method to detect and count immunostained ganglion cells using a new
NABLA_N network based deep learning (DL) approach, called GanglionNet. The
morphological image analysis methods are applied for refinement of the regions
for counting of the cells and define ganglia regions (a set of ganglion cells)
from the predicted masks. The proposed model is trained with single point
annotated samples by the expert pathologist. The GanglionNet is tested on ten
completely new High Power Field (HPF) images with dimension of 2560x1920 pixels
and the outputs are compared against the manual counting results by the expert
pathologist. The proposed method shows a robust 97.49% detection accuracy for
ganglion cells, when compared to counts by the expert pathologist, which
demonstrates the robustness of GanglionNet. The proposed DL based ganglion cell
detection and counting method will simplify and standardize TZ diagnosis for HD
patients.

Image and Video Processing;Computer Vision and Pattern Recognition