Tracing and segmentation of molecular patterns in 3-dimensional cryo-et/em density maps through algorithmic image processing and deep learning-based techniques

• URL: http://arxiv.org/abs/2403.17293v1
• Date: Tue, 26 Mar 2024 00:41:54 GMT
• Title: Tracing and segmentation of molecular patterns in 3-dimensional cryo-et/em density maps through algorithmic image processing and deep learning-based techniques
• Authors: Salim Sazzed,
• Abstract summary: dissertation focuses on developing sophisticated computational techniques for tracing actin filaments. Three novel methodologies have been developed: BundleTrac, for tracing bundle-like actin filaments found in Stereocilium, Spaghetti Tracer, for tracing filaments that move individually with loosely cohesive movements, and Struwwel Tracer, for tracing randomly orientated actin filaments in the actin network. The second component of the dissertation introduces a convolutional neural network (CNN) based segmentation model to determine the location of protein secondary structures, such as helices and beta-sheets, in medium-resolution (5-10 Angstrom) 3-dimensional cryo-electron microscopy
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding the structures of biological macromolecules is highly important as they are closely associated with cellular functionalities. Comprehending the precise organization actin filaments is crucial because they form the dynamic cytoskeleton, which offers structural support to cells and connects the cell's interior with its surroundings. However, determining the precise organization of actin filaments is challenging due to the poor quality of cryo-electron tomography (cryo-ET) images, which suffer from low signal-to-noise (SNR) ratios and the presence of missing wedge, as well as diverse shape characteristics of actin filaments. To address these formidable challenges, the primary component of this dissertation focuses on developing sophisticated computational techniques for tracing actin filaments. In particular, three novel methodologies have been developed: i) BundleTrac, for tracing bundle-like actin filaments found in Stereocilium, ii) Spaghetti Tracer, for tracing filaments that move individually with loosely cohesive movements, and iii) Struwwel Tracer, for tracing randomly orientated actin filaments in the actin network. The second component of the dissertation introduces a convolutional neural network (CNN) based segmentation model to determine the location of protein secondary structures, such as helices and beta-sheets, in medium-resolution (5-10 Angstrom) 3-dimensional cryo-electron microscopy (cryo-EM) images. This methodology later evolved into a tool named DeepSSETracer. The final component of the dissertation presents a novel algorithm, cylindrical fit measure, to estimate image structure match at helix regions in medium-resolution cryo-EM images. Overall, my dissertation has made significant contributions to addressing critical research challenges in structural biology by introducing various computational methods and tools.

Related papers

• GPU-Accelerated RSF Level Set Evolution for Large-Scale Microvascular Segmentation [2.5003043942194236]
  We propose a reformulation and implementation of the region-scalable fitting (RSF) level set model. This makes it amenable to three-dimensional evaluation using both single-instruction multiple data (SIMD) and single-program multiple-data (SPMD) parallel processing. We tested this 3D parallel RSF approach on multiple data sets acquired using state-of-the-art imaging techniques to acquire microvascular data.
  arXiv  Detail & Related papers  (2024-04-03T15:37:02Z)
• PyNanospacing: TEM image processing tool for strain analysis and visualization [0.0]
  This paper develops a Python code for TEM image processing that can handle a wide range of materials. It converts local differences in interplanar spacings into contour maps allowing for a visual representation of lattice expansion and compression. The tool is very generic and can significantly aid in analyzing material properties using TEM images.
  arXiv  Detail & Related papers  (2023-11-27T12:08:46Z)
• Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy [2.0445155106382797]
  Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions. CNN based deep-learning models is a powerful technique to detect features from material micrographs in an automated manner. We combine scanning electron microscopy (SEM) images of 347H stainless steel as training data and electron backscatter diffraction (EBSD) micrographs as pixel-wise labels for grain boundary detection.
  arXiv  Detail & Related papers  (2023-05-12T22:49:36Z)
• 3D Mitochondria Instance Segmentation with Spatio-Temporal Transformers [101.44668514239959]
  We propose a hybrid encoder-decoder framework that efficiently computes spatial and temporal attentions in parallel. We also introduce a semantic clutter-background adversarial loss during training that aids in the region of mitochondria instances from the background.
  arXiv  Detail & Related papers  (2023-03-21T17:58:49Z)
• Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics [77.34726150561087]
  This work proposes a generative adversarial network tailored towards three-dimensional microstructure generation. The lightweight algorithm is able to learn the underlying properties of the material from a single microCT-scan without the need of explicit descriptors.
  arXiv  Detail & Related papers  (2022-05-31T13:26:51Z)
• 3D Reconstruction of Curvilinear Structures with Stereo Matching DeepConvolutional Neural Networks [52.710012864395246]
  We propose a fully automated pipeline for both detection and matching of curvilinear structures in stereo pairs. We mainly focus on 3D reconstruction of dislocations from stereo pairs of TEM images.
  arXiv  Detail & Related papers  (2021-10-14T23:05:47Z)
• 3D Structure from 2D Microscopy images using Deep Learning [0.0]
  Recent advances in Artificial Intelligence have been applied to retrieving accurate 3D structures from microscopy images. Herewe present a deep learning solution for reconstructing the protein com-plexes from a number of 2D single molecule localization microscopy images.
  arXiv  Detail & Related papers  (2021-10-14T14:55:41Z)
• Disentangling semantic features of macromolecules in Cryo-Electron Tomography [7.804210995893708]
  Explicitly disentangling the semantic features of macromolecules is crucial for performing several downstream analyses on the macromolecules. This paper proposes a 3D Spatial Variational Autoencoder that explicitly disentangle the structure, orientation, and shift of macromolecules.
  arXiv  Detail & Related papers  (2021-06-27T10:41:26Z)
• ATOM3D: Tasks On Molecules in Three Dimensions [91.72138447636769]
  Deep neural networks have recently gained significant attention. In this work we present ATOM3D, a collection of both novel and existing datasets spanning several key classes of biomolecules. We develop three-dimensional molecular learning networks for each of these tasks, finding that they consistently improve performance.
  arXiv  Detail & Related papers  (2020-12-07T20:18:23Z)
• CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging [90.78899127463445]
  We propose a generic and unified convolution neural network for the segmentation of curvilinear structures. We introduce a new curvilinear structure segmentation network (CS2-Net), which includes a self-attention mechanism in the encoder and decoder.
  arXiv  Detail & Related papers  (2020-10-15T03:06:37Z)
• Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting [86.33696045574692]
  We propose a Cycle Consistency Panoptic Domain Adaptive Mask R-CNN (CyC-PDAM) architecture for unsupervised nuclei segmentation in histopathology images. We first propose a nuclei inpainting mechanism to remove the auxiliary generated objects in the synthesized images. Secondly, a semantic branch with a domain discriminator is designed to achieve panoptic-level domain adaptation.
  arXiv  Detail & Related papers  (2020-05-05T11:08:26Z)

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.