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Pàgina inicial > Articles > Articles publicats > A Benchmark for endoluminal scene segmentation of colonoscopy images |
Data: | 2017 |
Resum: | Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss rate and the inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing decision support systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endoluminal scene, targeting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCNs). We perform a comparative study to show that FCNs significantly outperform, without any further postprocessing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization. |
Ajuts: | Ministerio de Ciencia e Innovación TRA/2014-57088-C2-1-R Ministerio de Economía y Competitividad DPI/2015-65286-R Agència de Gestió d'Ajuts Universitaris i de Recerca 2014-SGR-1506 Agència de Gestió d'Ajuts Universitaris i de Recerca 2014-SGR-1470 Agència de Gestió d'Ajuts Universitaris i de Recerca 2014-SGR-135 |
Drets: | Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. |
Llengua: | Anglès |
Document: | Article ; recerca ; Versió publicada |
Publicat a: | Journal of Healthcare Engineering, Vol. 2017 (July 2017) , art. 4037190, ISSN 2040-2295 |
9 p, 2.1 MB |