Robust semi-automatic vessel tracing in the human retinal image by an instance segmentation neural network

• URL: http://arxiv.org/abs/2402.10055v1
• Date: Thu, 15 Feb 2024 16:25:28 GMT
• Title: Robust semi-automatic vessel tracing in the human retinal image by an instance segmentation neural network
• Authors: Siyi Chen, Amir H. Kashani, Ji Yi
• Abstract summary: We present a novel approach for a robust semi-automatic vessel tracing algorithm on human fundus images by an instance segmentation neural network (InSegNN) InSegNN separates and labels different vascular trees individually and therefore enable tracing each tree throughout its branching. We have demonstrated tracing individual vessel trees from fundus images, and simultaneously retain the vessel hierarchy information.
• Score: 1.324564545341267
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The morphology and hierarchy of the vascular systems are essential for perfusion in supporting metabolism. In human retina, one of the most energy-demanding organs, retinal circulation nourishes the entire inner retina by an intricate vasculature emerging and remerging at the optic nerve head (ONH). Thus, tracing the vascular branching from ONH through the vascular tree can illustrate vascular hierarchy and allow detailed morphological quantification, and yet remains a challenging task. Here, we presented a novel approach for a robust semi-automatic vessel tracing algorithm on human fundus images by an instance segmentation neural network (InSegNN). Distinct from semantic segmentation, InSegNN separates and labels different vascular trees individually and therefore enable tracing each tree throughout its branching. We have built-in three strategies to improve robustness and accuracy with temporal learning, spatial multi-sampling, and dynamic probability map. We achieved 83% specificity, and 50% improvement in Symmetric Best Dice (SBD) compared to literature, and outperformed baseline U-net. We have demonstrated tracing individual vessel trees from fundus images, and simultaneously retain the vessel hierarchy information. InSegNN paves a way for any subsequent morphological analysis of vascular morphology in relation to retinal diseases.

Related papers

• Building a Synthetic Vascular Model: Evaluation in an Intracranial Aneurysms Detection Scenario [0.8749675983608172]
  This model intends to provide a dataset of brain arteries which could be used by a 3D convolutional neural network to efficiently detect Intra-Cranial Aneurysms. In this work, we thoroughly describe the synthetic vasculature model, we build up a neural network designed for aneurysm segmentation and detection, and we carry out an in-depth evaluation of the performance gap gained thanks to the synthetic model data augmentation.
  arXiv  Detail & Related papers  (2024-11-04T18:08:24Z)
• A vascular synthetic model for improved aneurysm segmentation and detection via Deep Neural Networks [0.8749675983608172]
  Our goal was to provide a dataset of brain arteries which could be used by a 3D Convolutional Neural Network (CNN) Our model has been designed to simultaneously mimic the arteries geometry, the ICA shape and the background noise.
  arXiv  Detail & Related papers  (2024-03-27T16:22:45Z)
• Addressing caveats of neural persistence with deep graph persistence [54.424983583720675]
  We find that the variance of network weights and spatial concentration of large weights are the main factors that impact neural persistence. We propose an extension of the filtration underlying neural persistence to the whole neural network instead of single layers. This yields our deep graph persistence measure, which implicitly incorporates persistent paths through the network and alleviates variance-related issues.
  arXiv  Detail & Related papers  (2023-07-20T13:34:11Z)
• Deep Angiogram: Trivializing Retinal Vessel Segmentation [1.8479315677380455]
  We propose a contrastive variational auto-encoder that can filter out irrelevant features and synthesize a latent image, named deep angiogram. The generalizability of the synthetic network is improved by the contrastive loss that makes the model less sensitive to variations of image contrast and noisy features.
  arXiv  Detail & Related papers  (2023-07-01T06:13:10Z)
• Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation [48.638327652506284]
  Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. We present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach.
  arXiv  Detail & Related papers  (2022-11-12T05:39:17Z)
• Parametric Scaling of Preprocessing assisted U-net Architecture for Improvised Retinal Vessel Segmentation [1.3869502085838448]
  We present an image enhancement technique based on the morphological preprocessing coupled with a scaled U-net architecture. A significant improvement as compared to the other algorithms in the domain, in terms of the area under ROC curve (>0.9762) and classification accuracy (>95.47%) are evident from the results.
  arXiv  Detail & Related papers  (2022-03-18T15:26:05Z)
• Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction [57.74609918453932]
  Automatic blood vessel extraction from 3D medical images is crucial for vascular disease diagnoses. Existing methods may suffer from discontinuities of extracted vessels when segmenting such thin tubular structures from 3D images. We argue that preserving the continuity of extracted vessels requires to take into account the global geometry. We propose a hybrid representation learning approach to address this challenge.
  arXiv  Detail & Related papers  (2020-12-14T05:22:49Z)
• Rethinking the Extraction and Interaction of Multi-Scale Features for Vessel Segmentation [53.187152856583396]
  We propose a novel deep learning model called PC-Net to segment retinal vessels and major arteries in 2D fundus image and 3D computed tomography angiography (CTA) scans. In PC-Net, the pyramid squeeze-and-excitation (PSE) module introduces spatial information to each convolutional block, boosting its ability to extract more effective multi-scale features.
  arXiv  Detail & Related papers  (2020-10-09T08:22:54Z)
• Assignment Flow for Order-Constrained OCT Segmentation [0.0]
  The identification of retinal layer thicknesses serves as an essential task be done for each patient separately. The elaboration of automated segmentation models has become an important task in the field of medical image processing. We propose a novel, purely data driven textitgeometric approach to order-constrained 3D OCT retinal cell layer segmentation
  arXiv  Detail & Related papers  (2020-09-10T01:57:53Z)
• Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks [68.96150598294072]
  Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting premature infants with lower weights. It causes proliferation of vessels in the retina and could result in vision loss and, eventually, retinal detachment, leading to blindness. In recent years, there has been a significant effort to automate the diagnosis using deep learning. This paper builds upon the success of previous models and develops a novel architecture, which combines object segmentation and convolutional neural networks (CNN) Our proposed system first trains an object segmentation model to identify the demarcation line at a pixel level and adds the resulting mask as an additional "color" channel in
  arXiv  Detail & Related papers  (2020-04-03T14:07:41Z)
• clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation [57.20783326661043]
  We introduce a novel similarity measure termed centerlineDice (short clDice) We theoretically prove that clDice guarantees topology preservation up to homotopy equivalence for binary 2D and 3D segmentation. We benchmark the soft-clDice loss on five public datasets, including vessels, roads and neurons (2D and 3D)
  arXiv  Detail & Related papers  (2020-03-16T16:27:49Z)

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.