Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey

• URL: http://arxiv.org/abs/2206.07171v3
• Date: Thu, 30 Mar 2023 08:06:56 GMT
• Title: Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey
• Authors: Anusha Aswath, Ahmad Alsahaf, Ben N. G. Giepmans, George Azzopardi
• Abstract summary: deep learning algorithms achieved impressive results in both pixel-level labeling and the labeling of separate instances of the same class. In this review, we examine how these algorithms were adapted to the task of segmenting cellular and sub-cellular structures in EM images.
• Score: 6.144134660210243
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated and semi-automated techniques in biomedical electron microscopy (EM) enable the acquisition of large datasets at a high rate. Segmentation methods are therefore essential to analyze and interpret these large volumes of data, which can no longer completely be labeled manually. In recent years, deep learning algorithms achieved impressive results in both pixel-level labeling (semantic segmentation) and the labeling of separate instances of the same class (instance segmentation). In this review, we examine how these algorithms were adapted to the task of segmenting cellular and sub-cellular structures in EM images. The special challenges posed by such images and the network architectures that overcame some of them are described. Moreover, a thorough overview is also provided on the notable datasets that contributed to the proliferation of deep learning in EM. Finally, an outlook of current trends and future prospects of EM segmentation is given, especially in the area of label-free learning.

Related papers

• ShapeMamba-EM: Fine-Tuning Foundation Model with Local Shape Descriptors and Mamba Blocks for 3D EM Image Segmentation [49.42525661521625]
  This paper presents ShapeMamba-EM, a specialized fine-tuning method for 3D EM segmentation. It is tested over a wide range of EM images, covering five segmentation tasks and 10 datasets.
  arXiv  Detail & Related papers  (2024-08-26T08:59:22Z)
• Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey [49.47197748663787]
  This review aims to provide a first comprehensive and organized overview of the state-of-the-art research results on pseudo-label methods in the field of semi-supervised semantic segmentation. In addition, we explore the application of pseudo-label technology in medical and remote-sensing image segmentation.
  arXiv  Detail & Related papers  (2024-03-04T10:18:38Z)
• Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation [48.267001230607306]
  We propose a pretraining framework that enhances multiscale consistency in EM volumes. Our approach leverages a Siamese network architecture, integrating strong and weak data augmentations. It effectively captures voxel and feature consistency, showing promise for learning transferable representations for EM analysis.
  arXiv  Detail & Related papers  (2023-08-19T05:49:13Z)
• A Knowledge Distillation framework for Multi-Organ Segmentation of Medaka Fish in Tomographic Image [5.881800919492064]
  We propose a self-training framework for multi-organ segmentation in tomographic images of Medaka fish. We utilize the pseudo-labeled data from a pretrained model and adopt a Quality Teacher to refine the pseudo-labeled data. The experimental results demonstrate that our method improves mean Intersection over Union (IoU) by 5.9% on the full dataset.
  arXiv  Detail & Related papers  (2023-02-24T10:31:29Z)
• Cell segmentation from telecentric bright-field transmitted light microscopic images using a Residual Attention U-Net: a case study on HeLa line [0.0]
  Living cell segmentation from bright-field light microscopic images is challenging due to the image complexity and temporal changes in the living cells. Recently developed deep learning (DL)-based methods became popular in medical and microscopic image segmentation tasks due to their success and promising outcomes. The main objective of this paper is to develop a deep learning, UNet-based method to segment the living cells of the HeLa line in bright-field transmitted light microscopy.
  arXiv  Detail & Related papers  (2022-03-23T09:20:30Z)
• Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation [94.16816278191477]
  We present a framework for semi-adaptive and domain-supervised semantic segmentation. It is enhanced by self-supervised monocular depth estimation trained only on unlabeled image sequences. We validate the proposed model on the Cityscapes dataset.
  arXiv  Detail & Related papers  (2021-08-28T01:33:38Z)
• Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation [90.87105131054419]
  We present a framework for semi-supervised semantic segmentation, which is enhanced by self-supervised monocular depth estimation from unlabeled image sequences. We validate the proposed model on the Cityscapes dataset, where all three modules demonstrate significant performance gains.
  arXiv  Detail & Related papers  (2020-12-19T21:18:03Z)
• Multiclass Yeast Segmentation in Microstructured Environments with Deep Learning [20.456742449675904]
  We present convolutional neural networks trained for multiclass segmenting of individual yeast cells. We showcase the method's contribution to segmenting yeast in microstructured environments with a typical synthetic biology application.
  arXiv  Detail & Related papers  (2020-11-16T16:16:13Z)
• A comparative study of semi- and self-supervised semantic segmentation of biomedical microscopy data [0.13701366534590495]
  Convolutional Neural Networks (CNNs) have become the state-of-the-art method for biomedical image analysis. These networks are usually trained in a supervised manner, requiring large amounts of labelled training data. In this work, we validate alternative ways to train CNNs with fewer labels for biomedical image segmentation using.
  arXiv  Detail & Related papers  (2020-11-11T20:57:10Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.