GanglionNet: Objectively Assess the Density and Distribution of Ganglion Cells With NABLA-N Network

TL;DR

GanglionNet is a deep learning-based tool that automates the detection and counting of ganglion cells in histological images, aiming to improve accuracy and efficiency in diagnosing Hirschsprung's disease.

Contribution

This paper introduces GanglionNet, a novel deep learning model trained on expert-annotated data for automated ganglion cell detection and counting in histological sections.

Findings

Achieved 97.49% detection accuracy compared to expert counts

Demonstrated robustness on new high-resolution images

Simplifies and standardizes transition zone diagnosis

Abstract

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.