Immunofluorescence Capillary Imaging Segmentation: Cases Study

• URL: http://arxiv.org/abs/2207.06861v1
• Date: Thu, 14 Jul 2022 12:29:52 GMT
• Title: Immunofluorescence Capillary Imaging Segmentation: Cases Study
• Authors: Runpeng Hou, Ziyuan Ye, Chengyu Yang, Linhao Fu, Chao Liu, and Quanying Liu
• Abstract summary: We present a benchmark dataset, named IFCIS-155, consisting of 155 2D capillary images with segmentation boundaries and vessel fillings annotated by biomedical experts. We leverage state-of-the-art immunofluorescence imaging techniques to highlight the rich vascular morphology of interosseous capillaries. Our work offers a benchmark dataset for training deep learning models for capillary image segmentation and provides a potential tool for future capillary research.
• Score: 4.6841801126064455
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Nonunion is one of the challenges faced by orthopedics clinics for the technical difficulties and high costs in photographing interosseous capillaries. Segmenting vessels and filling capillaries are critical in understanding the obstacles encountered in capillary growth. However, existing datasets for blood vessel segmentation mainly focus on the large blood vessels of the body, and the lack of labeled capillary image datasets greatly limits the methodological development and applications of vessel segmentation and capillary filling. Here, we present a benchmark dataset, named IFCIS-155, consisting of 155 2D capillary images with segmentation boundaries and vessel fillings annotated by biomedical experts, and 19 large-scale, high-resolution 3D capillary images. To obtain better images of interosseous capillaries, we leverage state-of-the-art immunofluorescence imaging techniques to highlight the rich vascular morphology of interosseous capillaries. We conduct comprehensive experiments to verify the effectiveness of the dataset and the benchmarking deep learning models (\eg UNet/UNet++ and the modified UNet/UNet++). Our work offers a benchmark dataset for training deep learning models for capillary image segmentation and provides a potential tool for future capillary research. The IFCIS-155 dataset and code are all publicly available at \url{https://github.com/ncclabsustech/IFCIS-55}.

Related papers

• Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging [33.23991248643144]
  We present a thorough literature review, highlighting the state of machine learning techniques across diverse organs. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation in a new imaging modality. HiP CT enables 3D imaging of complete organs at an unprecedented resolution of ca. 20mm per voxel.
  arXiv  Detail & Related papers  (2023-11-22T11:15:38Z)
• Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations [12.571349114534597]
  We present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets.
  arXiv  Detail & Related papers  (2023-06-19T14:01:47Z)
• 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)
• FetReg2021: A Challenge on Placental Vessel Segmentation and Registration in Fetoscopy [52.3219875147181]
  Fetoscopic laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS) The procedure is particularly challenging due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility, and variability in illumination. Computer-assisted intervention (CAI) can provide surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fet
  arXiv  Detail & Related papers  (2022-06-24T23:44:42Z)
• FetReg: Placental Vessel Segmentation and Registration in Fetoscopy Challenge Dataset [57.30136148318641]
  Fetoscopy laser photocoagulation is a widely used procedure for the treatment of Twin-to-Twin Transfusion Syndrome (TTTS) This may lead to increased procedural time and incomplete ablation, resulting in persistent TTTS. Computer-assisted intervention may help overcome these challenges by expanding the fetoscopic field of view through video mosaicking and providing better visualization of the vessel network. We present a large-scale multi-centre dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms for the fetal environment with a focus on creating drift-free mosaics from long duration fetoscopy videos.
  arXiv  Detail & Related papers  (2021-06-10T17:14:27Z)
• Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentation [58.45470500617549]
  We propose a hierarchical deep network where an attention mechanism localizes the low-contrast capillary regions guided by the whole vessels. The proposed method achieves the state-of-the-art performance in the benchmarks of both retinal artery/vein segmentation in fundus images and liver portal/hepatic vessel segmentation in CT images.
  arXiv  Detail & Related papers  (2021-05-31T06:55:43Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• 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)
• ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model [41.444917622855606]
  We release a dedicated OCT-A SEgmentation dataset (ROSE), which consists of 229 OCT-A images with vessel annotations at either centerline-level or pixel level. Secondly, we propose a novel Split-based Coarse-to-Fine vessel segmentation network (SCF-Net), with the ability to detect thick and thin vessels separately. In the SCF-Net, a split-based coarse segmentation (SCS) module is first introduced to produce a preliminary confidence map of vessels, and a split-based refinement (SRN) module is then used to optimize the shape/contour of
  arXiv  Detail & Related papers  (2020-07-10T06:54:19Z)

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.