FoldIt: Haustral Folds Detection and Segmentation in Colonoscopy Videos

• URL: http://arxiv.org/abs/2106.12522v1
• Date: Wed, 23 Jun 2021 16:41:10 GMT
• Title: FoldIt: Haustral Folds Detection and Segmentation in Colonoscopy Videos
• Authors: Shawn Mathew, Saad Nadeem, Arie Kaufman
• Abstract summary: Haustral folds are colon wall protrusions implicated for high polyp miss rate during optical colonoscopy procedures. We present a novel generative adversarial network, FoldIt, for feature-consistent image translation of optical colonoscopy videos to virtual colonoscopy renderings with haustral fold overlays.
• Score: 6.187780920448871
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Haustral folds are colon wall protrusions implicated for high polyp miss rate during optical colonoscopy procedures. If segmented accurately, haustral folds can allow for better estimation of missed surface and can also serve as valuable landmarks for registering pre-treatment virtual (CT) and optical colonoscopies, to guide navigation towards the anomalies found in pre-treatment scans. We present a novel generative adversarial network, FoldIt, for feature-consistent image translation of optical colonoscopy videos to virtual colonoscopy renderings with haustral fold overlays. A new transitive loss is introduced in order to leverage ground truth information between haustral fold annotations and virtual colonoscopy renderings. We demonstrate the effectiveness of our model on real challenging optical colonoscopy videos as well as on textured virtual colonoscopy videos with clinician-verified haustral fold annotations. All code and scripts to reproduce the experiments of this paper will be made available via our Computational Endoscopy Platform at https://github.com/nadeemlab/CEP.

Related papers

• Frontiers in Intelligent Colonoscopy [96.57251132744446]
  This study investigates the frontiers of intelligent colonoscopy techniques and their prospective implications for multimodal medical applications. We assess the current data-centric and model-centric landscapes through four tasks for colonoscopic scene perception. To embrace the coming multimodal era, we establish three foundational initiatives: a large-scale multimodal instruction tuning dataset ColonINST, a colonoscopy-designed multimodal language model ColonGPT, and a multimodal benchmark.
  arXiv  Detail & Related papers  (2024-10-22T17:57:12Z)
• ToDER: Towards Colonoscopy Depth Estimation and Reconstruction with Geometry Constraint Adaptation [67.22294293695255]
  We propose a novel reconstruction pipeline with a bi-directional adaptation architecture named ToDER to get precise depth estimations. Experimental results demonstrate that our approach can precisely predict depth maps in both realistic and synthetic colonoscopy videos.
  arXiv  Detail & Related papers  (2024-07-23T14:24:26Z)
• CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
  We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism. We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
  arXiv  Detail & Related papers  (2024-03-21T15:13:36Z)
• AiAReSeg: Catheter Detection and Segmentation in Interventional Ultrasound using Transformers [75.20925220246689]
  endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature. This work proposes a solution using an adaptation of a state-of-the-art machine learning transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences.
  arXiv  Detail & Related papers  (2023-09-25T19:34:12Z)
• ColNav: Real-Time Colon Navigation for Colonoscopy [0.0]
  This paper presents a novel real-time navigation guidance system for Optical Colonoscopy (OC) Our proposed system employs a real-time approach that displays both an unfolded representation of the colon and a local indicator directing to un-inspected areas. Our system resulted in a higher polyp recall (PR) and high inter-rater reliability with physicians for coverage prediction.
  arXiv  Detail & Related papers  (2023-06-07T09:09:35Z)
• Development of an Immersive Virtual Colonoscopy Viewer for Colon Growths Diagnosis [0.4771971685916732]
  We present a new design exploring elements of the VR paradigm to make the immersive analysis more efficient while still effective. We also plan the conduction of experiments with experts to assess the multi-factor influences of coverage, duration, and diagnostic accuracy.
  arXiv  Detail & Related papers  (2023-02-06T17:28:30Z)
• Colonoscopy Landmark Detection using Vision Transformers [0.0]
  We have collected a dataset of 120 videos and 2416 snapshots taken during the procedure. We have developed a novel, vision-transformer based landmark detection algorithm. We report an accuracy of 82% with the vision transformer backbone on a test dataset of snapshots.
  arXiv  Detail & Related papers  (2022-09-22T20:39:07Z)
• Deep Learning-based Biological Anatomical Landmark Detection in Colonoscopy Videos [21.384094148149003]
  We propose a novel deep learning-based approach to detect biological anatomical landmarks in colonoscopy videos. Average detection accuracy reaches 99.75%, while the average IoU of 0.91 shows a high degree of similarity between our predicted landmark periods and ground truth.
  arXiv  Detail & Related papers  (2021-08-06T05:52:32Z)
• Colorectal Polyp Classification from White-light Colonoscopy Images via Domain Alignment [57.419727894848485]
  A computer-aided diagnosis system is required to assist accurate diagnosis from colonoscopy images. Most previous studies at-tempt to develop models for polyp differentiation using Narrow-Band Imaging (NBI) or other enhanced images. We propose a novel framework based on a teacher-student architecture for the accurate colorectal polyp classification.
  arXiv  Detail & Related papers  (2021-08-05T09:31:46Z)
• NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy [0.6125117548653111]
  We propose NanoNet, a novel architecture for the segmentation of video capsule endoscopy and colonoscopy images. Our proposed architecture allows real-time performance and has higher segmentation accuracy compared to other more complex ones.
  arXiv  Detail & Related papers  (2021-04-22T15:40:28Z)
• Colonoscopy Polyp Detection: Domain Adaptation From Medical Report Images to Real-time Videos [76.37907640271806]
  We propose an Image-video-joint polyp detection network (Ivy-Net) to address the domain gap between colonoscopy images from historical medical reports and real-time videos. Experiments on the collected dataset demonstrate that our Ivy-Net achieves the state-of-the-art result on colonoscopy video.
  arXiv  Detail & Related papers  (2020-12-31T10:33:09Z)

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.