Mathematics and Computer Science, Volume 2

19Image Segmentation of Neuronal Cell with Ensemble Unet Architecture

Kirtan Kanani¹, Aditya K. Gupta¹, Ankit Kumar Nikum^1⋆, Prashant Gupta² and Dharmik Raval¹

¹Department of Mechanical, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India

²Licious, Banglore, India

Abstract

Medical image segmentation consists of heterogeneous pixel intensities, noisy/ ill-defined borders, and high variability, which are significant technical obstacles for segmentation. Also, generally the requirement of annotated samples by the networks is significantly large to achieve high accuracy. Gathering this dataset for the particular application and annotating new images is both time-consuming and costly. Unet solves this problem by not requiring vast datasets for picture segmentation. The present work describes the use of a network that depends on augmentation of the existing annotated dataset to make better use of these examples and a comparison of encoder accuracy on Unet is presented. The encoder principal function is to reduce image dimensionality while keeping as much information as possible. EfficientNets tackles both of these issues and utilizing it as an encoder of Unet can further enhance its accuracy. The test dataset highest F1-Score and IoU were 0.7655 and 0.6201 on neuronal data values, respectively. It outperforms Inception and ResNet encoder networks with considerably more parameters and a higher inference time.

Keywords: Image segmentation, computer vision, deep ...