VGG-UNet/VGG-SegNet Supported Automatic Segmentation of Endoplasmic Reticulum Network in Fluorescence Microscopy Images

Abstract

This research work aims to implement an automated segmentation process to extract the endoplasmic reticulum (ER) network in fluorescence microscopy images (FMI) using pretrained convolutional neural network (CNN). The threshold level of the raw FMT is complex, and extraction of the ER network is a challenging task. Hence, an image conversion procedure is initially employed to reduce its complexity. This work employed the pretrained CNN schemes, such as VGG-UNet and VGG-SegNet, to mine the ER network from the chosen FMI test images. The proposed ER segmentation pipeline consists of the following phases; (i) clinical image collection, 16-bit to 8-bit conversion and resizing; (ii) implementation of pretrained VGG-UNet and VGG-SegNet; (iii) extraction of the binary form of ER network; (iv) comparing the mined ER with ground-truth; and (v) computation of image measures and validation. The considered FMI dataset consists of 223 test images, and image augmentation is then implemented to increase these images. The result of this scheme is then confirmed against other CNN methods, such as U-Net, SegNet, and Res-UNet. The experimental outcome confirms a segmentation accuracy of >98% with VGG-UNet and VGG-SegNet. The results of this research authenticate that the proposed pipeline can be considered to examine the clinical-grade FMI.

Enhanced Karyotyping Through Deep Learning-Assisted Segmentation and Classification of Chromosomal Cells

“…In this research, we plan to create a framework by integrating the Convolutional Neural Network -supported Segmenting and classification to enhance the results of CC karyotyping by integrating CNN-supported segmentation and classification approaches. A new feature of this work is the implementation of VGG-UNet (VGG19 acts as the backbone) for segmenting the CC region and extracting the HF, including GLCMs and DWTs [8][9][10]. In this scheme, the pretrained VGG19 functions as the backbone (encoder) while the decoder is constructed based on the encoder structure.…”

Environmental Interference Suppression by Hybrid Segmentation Algorithm for Open-Area Electromagnetic Capability Testing

“…After separating different signals in the frequency domain, segmentation can be applied for further processing, which can generally be divided into two classes, semantic segmentation [6] and instance segmentation [7]. Semantic segmentation labels all image pixels with a set of object categories, and targets in the same category can not be distinguished.…”