Abstract
The quantitative study of cell morphology is of great importance as the structure and condition of cells and their structures can be related to conditions of health or disease. The first step towards that, is the accurate segmentation of cell structures. In this work, we compare five approaches, one traditional and four deep-learning, for the semantic segmentation of the nuclear envelope of cervical cancer cells commonly known as HeLa cells. Images of a HeLa cancer cell were semantically segmented with one traditional image-processing algorithm and four three deep learning architectures: VGG16, ResNet18, Inception-ResNet-v2, and U-Net. Three hundred slices, each 2000 × 2000 pixels, of a HeLa Cell were acquired with Serial Block Face Scanning Electron Microscopy. The first three deep learning architectures were pre-trained with ImageNet and then fine-tuned with transfer learning. The U-Net architecture was trained from scratch with 36, 000 training images and labels of size 128 × 128. The image-processing algorithm followed a pipeline of several traditional steps like edge detection, dilation and morphological operators. The algorithms were compared by measuring pixel-based segmentation accuracy and Jaccard index against a labelled ground truth. The results indicated a superior performance of the traditional algorithm (Accuracy = 99%, Jaccard = 93%) over the deep learning architectures: VGG16 (93%, 90%), ResNet18 (94%, 88%), Inception-ResNet-v2 (94%, 89%), and U-Net (92%, 56%).
Highlights
The study of cells and their organelles has interested scientists from the early days of Hooke and van Leeuwenhoek to the formulation of cell theory by Schleiden and Schwann [1]
Both metrics arise from the allocation of classes to every pixel of an image, for which four cases exist: (i) true positive (TP), correspond to pixels which were correctly predicted as a certain class or to have a condition present, (ii) true negative, (TN) corresponds to a pixel that was correctly predicted to be background or for which the condition not present, (iii) false positive, (FP) correspond to those pixels predicted to be a class but correspond to background, and (iv) false negative (FN), correspond to those pixels that were predicted to be background but in reality belong to a class
The first pre-trained deep neural network architectures, VGG16, ResNet18 and Inception-ResNet-v2 were trained in ImageNet and fine-tuned for semantic segmentation of the HeLa cells
Summary
The study of cells and their organelles has interested scientists from the early days of Hooke and van Leeuwenhoek to the formulation of cell theory by Schleiden and Schwann [1]. Four deep learning architectures, VGG16 [46], ResNet18 [70], and InceptionResNet-v2 [71], and U-Net [58] were used to perform the semantic segmentation of HeLa cells. The U-Net architecture contains two paths, first one path that contracts by reducing the size of the input images through which the context is capture, and a second expanding path; symmetric to the first, through which precise localisation is obtained These networks were selected because of their good balance between accuracy and computational complexity, especially ResNet and Inception-ResNet-v2, which outperform other common configurations and are at the Pareto frontier considering accuracy and complexity [72,73,74]. The EM data is available through EMPIAR (see S1 Code)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
