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Kvasir-SEG: A Segmented Polyp Dataset

Jha, Debesh ; Smedsrud, Pia H. ; Riegler, Michael A. ; Halvorsen, Pål ; de Lange, Thomas ; Johansen, Dag ; Johansen, Håvard D.

Lecture notes in computer science, 2020, p.451-462 [Periódico revisado por pares]

Cham: Springer International Publishing

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Título:
Kvasir-SEG: A Segmented Polyp Dataset
Autor: Jha, Debesh ; Smedsrud, Pia H. ; Riegler, Michael A. ; Halvorsen, Pål ; de Lange, Thomas ; Johansen, Dag ; Johansen, Håvard D.
Assuntos: Clinical medical sciences: 750 ; Computer technology: 551 ; Datateknologi: 551 ; Fuzzy C-mean clustering ; Gasteroenterologi: 773 ; Gastroenterology: 773 ; Informasjons- og kommunikasjonsteknologi: 550 ; Informasjons- og kommunikasjonsvitenskap: 420 ; Information and communication science: 420 ; Information and communication technology: 550 ; Klinisk medisinske fag: 750 ; Kvasir-SEG dataset ; Matematikk og naturvitenskap: 400 ; Mathematics and natural scienses: 400 ; Medical images ; Medisinske fag: 700 ; Midical sciences: 700 ; Polyp segmentation ; ResUNet ; Semantic segmentation ; Simulation, visualisation, signal processing, image analysis: 429 ; Simulering, visualisering, signalbehandling, bildeanalyse: 429 ; Technology: 500 ; Teknologi: 500 ; VDP
É parte de: Lecture notes in computer science, 2020, p.451-462
Notas: Lecture Notes in Computer Science (LNCS)
Descrição: Pixel-wise image segmentation is a highly demanding task in medical-image analysis. In practice, it is difficult to find annotated medical images with corresponding segmentation masks. In this paper, we present Kvasir-SEG: an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated by a medical doctor and then verified by an experienced gastroenterologist. Moreover, we also generated the bounding boxes of the polyp regions with the help of segmentation masks. We demonstrate the use of our dataset with a traditional segmentation approach and a modern deep-learning based Convolutional Neural Network (CNN) approach. The dataset will be of value for researchers to reproduce results and compare methods. By adding segmentation masks to the Kvasir dataset, which only provide frame-wise annotations, we enable multimedia and computer vision researchers to contribute in the field of polyp segmentation and automatic analysis of colonoscopy images.
Títulos relacionados: Lecture Notes in Computer Science
Editor: Cham: Springer International Publishing
Idioma: Inglês

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Kvasir-SEG: A Segmented Polyp Dataset

Jha, Debesh ; Smedsrud, Pia H. ; Riegler, Michael A. ; Halvorsen, Pål ; de Lange, Thomas ; Johansen, Dag ; Johansen, Håvard D.

Cham: Springer International Publishing

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