Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information

By Nima Tajbakhsh, Suryakanth R. Gurudu, Jianming Liang. IEEE Transactions on Medical Imaging, 2016

1,154 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Oncology

View Paper: https://www.semanticscholar.org/paper/9993e93f69050c2faa2985b94040f72ac311e82b

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In Oncology

Citations in the corpus

Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers (2021, methodology): “The images from top to bottom are from ClinicDB [10], ETIS [11], and ColonDB [12], which show that our model has better generalization ability.”
A Comprehensive Study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation (2021, positive): “2) We validate the extended architecture on a large range of datasets, i.e., Kvasir-SEG [4], CVC-ClinicDB [11], CVC-ColonDB [12], EITS-Larib [13], ASU-Mayo Clinic Colonoscopy Video Database [14] and CVC-VideoClinicDB [15], [16], and we compare our proposed approaches with the recent State-of-the-art”
TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation (2021, methodology): “Five public polyp datasets are chosen: Kvasir [9], CVC-ClinicDB [1], CVCColonDB [17], EndoScene [20] and ETIS [15].”
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].”
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].”
Kvasir-SEG: A Segmented Polyp Dataset (2019, background): “de our work and give future directions in Section 6. 2 Related Work There are only few available polyp datasets that consist of ground truth and corresponding segmentation mask. These are CVC-ColonDB [24], ASU-Mayo Clinic Colonoscopy Video c Database [3], ETIS-Larib Polyp DB [23], and CVCClinic DB [”
KVASIR: A Multi-Class Image Dataset for Computer Aided Gastrointestinal Disease Detection (2017, methodology): “In our earlier work [27, 32, 33], we have used the two only usable, publicly available GI tract datasets: the ASU-Mayo Clinic polyp data-base [35] and the CVC-ColonDB colonoscopy video database [7] The ASU-Mayo dataset consists of training and test sets with corresponding ground truth showing the…”
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? (2016, methodology): “(a) Comparison between incremental fine-tuning, training from scratch, and a handcrafted approach [42].”
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].”
Shallow Attention Network for Polyp Segmentation (2021, background): “, color and texture) [13,24,17], which can hardly capture the global context information and are not robust to complex scenarios.”
AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection (2023, positive): “AnomalyCLIP and VAND largely improve their detection and segmentation performance compared to that fine-tuned on MVTec AD, especially for the colon polyp-related datasets such as CVC-ClincDB, Kvasir, and Endo (note that these datasets are all from different domains compared to the fine-tuning ColonD”
Medical Image Segmentation via Cascaded Attention Decoding (2023, methodology): “EndoScene, ColonDB, and ETIS-LaribDB contain 60, 380, and 196 images, respectively.”
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images (2016, background): “The majority of available works on optical colonoscopy are focused on polyp detection (e.g. see [4,29,16,24,26,33]) and only few works address the problems of endoluminal scene segmentation.”
Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge (2017, neutral): “A graphical user interface was built for experts to label individually each frame from the testing videos of ASU-Mayo Clinic Colonoscopy Video © Database according to the mentioned image challenges.”
Cross-level Feature Aggregation Network for Polyp Segmentation (2023, methodology): “• CVC-ColonDB [22] : This dataset consists of 380 images with a size of 500 × 570 .”