Fully convolutional neural networks for polyp segmentation in colonoscopy

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

Novel experimental and software methods for image reconstruction and localization in capsule endoscopy

Background and study aims: Capsule endoscopy (CE) is invaluable for minimally invasive endoscopy of the gastrointestinal tract; however, several technological limitations remain including lack of reliable lesion localization. We present an approach to 3D reconstruction and localization using visual information from 2D CE images. Patients and methods: Colored thumbtacks were secured in rows to the internal wall of a LifeLike bowel model. A PillCam SB3 was calibrated and navigated linearly through the lumen by a high-precision robotic arm. The motion estimation algorithm used data (light falling on the object, fraction of reflected light and surface geometry) from 2D CE images in the video sequence to achieve 3D reconstruction of the bowel model at various frames. The ORB-SLAM technique was used for 3D reconstruction and CE localization within the reconstructed model. This algorithm compared pairs of points between images for reconstruction and localization. Results: As the capsule moved through the model bowel 42 to 66 video frames were obtained per pass. Mean absolute error in the estimated distance travelled by the CE was 4.1 ± 3.9 cm. Our algorithm was able to reconstruct the cylindrical shape of the model bowel with details of the attached thumbtacks. ORB-SLAM successfully reconstructed the bowel wall from simultaneous frames of the CE video. The “track” in the reconstruction corresponded well with the linear forwards-backwards movement of the capsule through the model lumen. Conclusion: The reconstruction methods, detailed above, were able to achieve good quality reconstruction of the bowel model and localization of the capsule trajectory using information from the CE video and images alone

Towards a Computed-Aided Diagnosis System in Colonoscopy: Automatic Polyp Segmentation Using Convolution Neural Networks

Early diagnosis is essential for the successful treatment of bowel cancers including colorectal cancer (CRC), and capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool when combined with automated image analysis. We present a deep learning rooted detection and segmentation framework for recognizing lesions in colonoscopy and capsule endoscopy images. We restructure established convolution architectures, such as VGG and ResNets, by converting them into fully-connected convolution networks (FCNs), fine-tune them and study their capabilities for polyp segmentation and detection. We additionally use shape-from-shading (SfS) to recover depth and provide a richer representation of the tissue’s structure in colonoscopy images. Depth is incorporated into our network models as an additional input channel to the RGB information and we demonstrate that the resulting network yields improved performance. Our networks are tested on publicly available datasets and the most accurate segmentation model achieved a mean segmentation interception over union (IU) of 47.78% and 56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp detection, the top performing models we propose surpass the current state-of-the-art with detection recalls superior to 90% for all datasets tested. To our knowledge, we present the first work to use FCNs for polyp segmentation in addition to proposing a novel combination of SfS and RGB that boosts performance

Nomenclature and semantic description of vascular lesions in small bowel capsule endoscopy: an international Delphi consensus statement

Background and study aims \u2002Nomenclature and descriptions of small bowel (SB) vascular lesions in capsule endoscopy (CE) are scarce in the medical literature. They are mostly based on the reader's opinion and thus differ between experts, with a potential negative impact on clinical care, teaching and research regarding SBCE. Our aim was to better define a nomenclature and to give a description of the most frequent vascular lesions in SBCE. Methods \u2002A panel of 18 European expert SBCE readers was formed during the UEGW 2016 meeting. Three experts constructed an Internet-based four-round Delphi consensus, but did not participate in the voting process. They built questionnaires that included various still frames of vascular lesions obtained with a third-generation SBCE system. The 15 remaining participants were asked to rate different proposals and description of the most common SB vascular lesions. A 6-point rating scale (varying from 'strongly disagree' to 'strongly agree') was used successive rounds. The consensus was reached when at least 80\u200a% voting members scored the statement within the 'agree' or 'strongly agree'. Results \u2002Consensual terms and descriptions were reached for angiectasia/angiodysplasia, erythematous patch, red spot/dot, and phlebectasia. A consensual description was reached for more subtle vascular lesions tentatively named "diminutive angiectasia" but no consensus was reached for this term. Conclusion \u2002An international group has reached a consensus on the nomenclature and descriptions of the most frequent and relevant SB vascular lesions in CE. These terms and descriptions are useful in daily practice, for teaching and for medical research purposes

Luminally expressed gastrointestinal biomarkers

Introduction: A biomarker is a measurable indicator of normal biologic processes, pathogenic processes or pharmacological responses. The identification of a useful biomarker is challenging, with several hurdles to overcome before clinical adoption. This review gives a general overview of a range of biomarkers associated with inflammatory bowel disease or colorectal cancer along the gastrointestinal tract. Areas covered: These markers include those that are already clinically accepted, such as inflammatory markers such as faecal calprotectin, S100A12 (Calgranulin C), Fatty Acid Binding Proteins (FABP), malignancy markers such as Faecal Occult Blood, Mucins, Stool DNA, Faecal microRNA (miRNA), other markers such as Faecal Elastase, Faecal alpha-1-antitrypsin, Alpha2-macroglobulin and possible future markers such as microbiota, volatile organic compounds and pH. Expert commentary: There are currently a few biomarkers that have been sufficiently validated for routine clinical use at present such as FC. However, many of these biomarkers continue to be limited in sensitivity and specificity for various GI diseases. Emerging biomarkers have the potential to improve diagnosis and monitoring but further study is required to determine efficacy and validate clinical utility