Kvasir-SEG: A Segmented Polyp Dataset

By Debesh Jha, Pia Helen Smedsrud, Michael Alexander Riegler, Pål Halvorsen, Thomas de Lange, Dag Johansen, Haavard D. Johansen. Conference on Multimedia Modeling, 2019

1,927 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Oncology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/349461d60b4d3d34dc97147e4b9ec2b9bd611be8

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Citations in the corpus

Adaptive Context Selection for Polyp Segmentation (2020, methodology): “The second is Kvasir-SEG Dataset [8] containing 1000 images with polyp regions.”
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation (2020, methodology): “The key challenges associated with medical image segmentation are the unavailability of a large number of annotated, lack of highquality labeled images for training [2], low image quality, lack of a standard segmentation protocol, and a large variations of images among patients [3].”
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation (2022, background): “…is that, in real-world applications, the acquired data 2D Microscope DSB[194] , MoNuSeg[195] Cross-patch dense contrastive learning framework Dt-DDCL, Zhang et al. [3] 2D Colonoscope kvasir-SEGdataset[196], Skin lesion dataset [197] Dual-task network for segmentation and lesion region inpainting.”
Automatic Polyp Segmentation via Multi-scale Subtraction Network (2021, methodology): “We evaluate the proposed model on five benchmark datasets: CVC-ColonDB [22], ETIS [21], Kvasir [11], CVC-T [24] and CVC-ClinicDB [2].”
EMCAD: Efficient Multi-Scale Convolutional Attention Decoding for Medical Image Segmentation (2024, methodology): “We resize images to 352 × 352 and use a multi-scale { 0.75, 1.0, 1.25 } training strategy with a gradient clip limit of 0.5 for ClinicDB [3], Kvasir [29], ColonDB [51], ETIS [51], BKAI [40], ISIC17 [15], and ISIC18 [15], while we resize images to 256 × 256 for BUSI [1], EM [6], and DSB18 [4].”
TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation (2021, methodology): “…the eﬀectiveness of TransFuse, four segmentation tasks with diﬀerent imaging modalities, disease types, target objects, target sizes, etc. are considered: 1) Polyp Segmentation , where ﬁve public polyp datasets are used: Kvasir [14], CVC-ClinicDB [2], CVC-ColonDB [24], EndoScene [27] and ETIS [21].”
HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy (2019, methodology): “If the reader wants information about classification and segmentation approaches and experiments comparing state of the art methods using parts of this dataset, the reader is referred to other studies 49 .”
Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers (2021, methodology): “• Finally, we conduct extensive experiments on five challenging benchmark datasets, including Kvasir-SEG [13], ClinicDB [10], ColonDB [12], Endoscene [14], and ETIS [11], to evaluate the performance of the proposed Polyp-PVT.”
A Comprehensive Study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation (2021, methodology): “in earlier research [4], [18], [20], [22], [26], [51] and in competitions like GIANA,1 comparing the correctly and wrongly identified pixels of findings.”
Transformers in Medical Image Analysis: A Review (2022, background): “Abbreviation: ACT-MOS, abdominal CT multi-organ segmentation; COPLE, COVID-19 Pneumonia Lesion segmentation; ACDC, Automated cardiac diagnosis challenge; MSD-01, Medical Segmentation Decathlon dataset Task01; KCCEE, Kvasir [63], CVC-ClinicDB [64], CVC-ColonDB [65], EndoScene [66] and ETIS [67].”
UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation (2021, positive): “We validate our method with five famous polyp segmentation benchmarks, Kvasir, CVC-ClinicDB, ETIS, CVC-ColonDB and CVC-300 and achieve state-of-the-art performance among previous methods. ar X iv :2 10 7.”
Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions (2024, background): “Zhou et al. [51] evaluate the performance of SAM with unprompted setting in segmenting polyps from colonoscopy images on five benchmark datasets [52, 53, 54, 55, 56].”
CaraNet: context axial reverse attention network for segmentation of small medical objects (2021, methodology): “We choose five polyp datasets: ETIS [10], CVC-ClinicDB [11], CVC-ColonDB [12], EndoScene [13] and Kvasir [14].”
Cross-level Feature Aggregation Network for Polyp Segmentation (2023, methodology): “C l i n i c D B [ 47 ] K v a s i r [ 49 ] m D i c e”
ResUNet++: An Advanced Architecture for Medical Image Segmentation (2019, methodology): “2) We annotated the polyp class from the Kvasir dataset [7] with the help of an expert gastroenterologist to create the new Kvasir-SEG dataset [8].”
DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation (2021, methodology): “Polyp Segmentation: For polyp segmentation task, we select ﬁve public polyp datasets including Kvasir [33], CVC-ColonDB (ColonDB) [34], EndoScene [35], ETIS [36], and CVC-ClinicDB (ClinicDB) [37].”