Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy

• URL: http://arxiv.org/abs/2011.08065v1
• Date: Fri, 23 Oct 2020 18:14:36 GMT
• Title: Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy
• Authors: Debesh Jha, Sharib Ali, Krister Emanuelsen, Steven A. Hicks, VajiraThambawita, Enrique Garcia-Ceja, Michael A. Riegler, Thomas de Lange, Peter T. Schmidt, H{\aa}vard D. Johansen, Dag Johansen, and P{\aa}l Halvorsen
• Abstract summary: Gastrointestinal (GI) pathologies are periodically screened, biopsied, and resected using surgical tools. This dataset consists of $590$ annotated frames containing GI procedure tools such as snares, balloons and biopsy forceps, etc.
• Score: 1.7579113628094125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gastrointestinal (GI) pathologies are periodically screened, biopsied, and resected using surgical tools. Usually the procedures and the treated or resected areas are not specifically tracked or analysed during or after colonoscopies. Information regarding disease borders, development and amount and size of the resected area get lost. This can lead to poor follow-up and bothersome reassessment difficulties post-treatment. To improve the current standard and also to foster more research on the topic we have released the ``Kvasir-Instrument'' dataset which consists of $590$ annotated frames containing GI procedure tools such as snares, balloons and biopsy forceps, etc. Beside of the images, the dataset includes ground truth masks and bounding boxes and has been verified by two expert GI endoscopists. Additionally, we provide a baseline for the segmentation of the GI tools to promote research and algorithm development. We obtained a dice coefficient score of 0.9158 and a Jaccard index of 0.8578 using a classical U-Net architecture. A similar dice coefficient score was observed for DoubleUNet. The qualitative results showed that the model did not work for the images with specularity and the frames with multiple instruments, while the best result for both methods was observed on all other types of images. Both, qualitative and quantitative results show that the model performs reasonably good, but there is a large potential for further improvements. Benchmarking using the dataset provides an opportunity for researchers to contribute to the field of automatic endoscopic diagnostic and therapeutic tool segmentation for GI endoscopy.

Related papers

• Unsupervised Machine Learning for Osteoporosis Diagnosis Using Singh Index Clustering on Hip Radiographs [0.0]
  Singh Index (SI) provides a straightforward, semi-quantitative means of osteoporosis diagnosis through plain hip radiographs. This study aims to automate SI identification from radiographs using machine learning algorithms.
  arXiv  Detail & Related papers  (2024-11-22T08:44:43Z)
• TotalSegmentator MRI: Sequence-Independent Segmentation of 59 Anatomical Structures in MR images [62.53931644063323]
  In this study we extended the capabilities of TotalSegmentator to MR images. We trained an nnU-Net segmentation algorithm on this dataset and calculated similarity coefficients (Dice) to evaluate the model's performance. The model significantly outperformed two other publicly available segmentation models (Dice score 0.824 versus 0.762; p0.001 and 0.762 versus 0.542; p)
  arXiv  Detail & Related papers  (2024-05-29T20:15:54Z)
• A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection [41.454028276986946]
  We propose a novel framework Two-Stage Generative Model (TSGM) to improve brain tumor detection and segmentation. CycleGAN is trained on unpaired data to generate abnormal images from healthy images as data prior. VE-JP is implemented to reconstruct healthy images using synthetic paired abnormal images as a guide.
  arXiv  Detail & Related papers  (2023-11-06T12:58:26Z)
• Revisiting Computer-Aided Tuberculosis Diagnosis [56.80999479735375]
  Tuberculosis (TB) is a major global health threat, causing millions of deaths annually. Computer-aided tuberculosis diagnosis (CTD) using deep learning has shown promise, but progress is hindered by limited training data. We establish a large-scale dataset, namely the Tuberculosis X-ray (TBX11K) dataset, which contains 11,200 chest X-ray (CXR) images with corresponding bounding box annotations for TB areas. This dataset enables the training of sophisticated detectors for high-quality CTD.
  arXiv  Detail & Related papers  (2023-07-06T08:27:48Z)
• Semi-supervised GAN for Bladder Tissue Classification in Multi-Domain Endoscopic Images [10.48945682277992]
  We propose a semi-surprised Generative Adrial Network (GAN)-based method composed of three main components. The overall average classification accuracy, precision, and recall obtained with the proposed method for tissue classification are 0.90, 0.88, and 0.89 respectively.
  arXiv  Detail & Related papers  (2022-12-21T21:32:36Z)
• Assessing Coarse-to-Fine Deep Learning Models for Optic Disc and Cup Segmentation in Fundus Images [0.0]
  coarse-to-fine deep learning algorithms are used to efficiently measure the vertical cup-to-disc ratio (vCDR) in fundus images. We present a comprehensive analysis of different coarse-to-fine designs for OD/OC segmentation using 5 public databases. Our analysis shows that these algorithms not necessarily outperfom standard multi-class single-stage models.
  arXiv  Detail & Related papers  (2022-09-28T19:19:16Z)
• Data-Efficient Vision Transformers for Multi-Label Disease Classification on Chest Radiographs [55.78588835407174]
  Vision Transformers (ViTs) have not been applied to this task despite their high classification performance on generic images. ViTs do not rely on convolutions but on patch-based self-attention and in contrast to CNNs, no prior knowledge of local connectivity is present. Our results show that while the performance between ViTs and CNNs is on par with a small benefit for ViTs, DeiTs outperform the former if a reasonably large data set is available for training.
  arXiv  Detail & Related papers  (2022-08-17T09:07:45Z)
• EndoMapper dataset of complete calibrated endoscopy procedures [8.577980383972005]
  This paper introduces the Endomapper dataset, the first collection of complete endoscopy sequences acquired during regular medical practice. Data will be used to build a 3D mapping and localization systems that can perform special task like, for example, detect blind zones during exploration.
  arXiv  Detail & Related papers  (2022-04-29T17:10:01Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• Malignancy Prediction and Lesion Identification from Clinical Dermatological Images [65.1629311281062]
  We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images. We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
  arXiv  Detail & Related papers  (2021-04-02T20:52:05Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)

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.