Colorectal cancer (CRC) is one of the most common and deadliest forms of cancer, accounting for nearly 10% of all forms of cancer in the world. Even though colonoscopy is considered the most effective method for screening and diagnosis, the success of the procedure is highly dependent on the operator skills and level of hand-eye coordination. In this work, we propose to adapt fully convolution neural networks (FCN), to identify and segment polyps in colonoscopy images. We converted three established networks into a fully convolution architecture and fine-tuned their learned representations to the polyp segmentation task. We validate our framework on the 2015 MICCAI polyp detection challenge dataset, surpassing the state-of-the-art in automated polyp detection. Our method obtained high segmentation accuracy and a detection precision and recall of 73.61% and 86.31%, respectively.
Patrick Brandao, Evangelos Mazomenos, Gastone Ciuti, Renato Caliò, Federico Bianchi, Arianna Menciassi, Paolo Dario, Anastasios Koulaouzidis, Alberto Arezzo, and Danail Stoyanov, "Fully convolutional neural networks for polyp segmentation in colonoscopy," Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340F (Presented at SPIE Medical Imaging: February 13, 2017; Published: 3 March 2017); https://doi.org/10.1117/12.2254361.
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Study of self-shadowing effect as a simple means to realize nanostructured thin films and layers with special attentions to birefringent obliquely deposited thin films and photo-luminescent porous silicon