Introduction: Nonmuscle-invasive bladder cancer has a relatively high postoperative recurrence rate despite the implementation of conventional treatment methods. Cystoscopy is essential for diagnos...

Support System of Cystoscopic Diagnosis for Bladder Cancer Based on Artificial Intelligence

Purpose: The objective of this paper was to develop a computer-aided diagnostic (CAD) tools for automated analysis of capsule endoscopic (CE) images, more precisely, detect small intestinal abnormalities like bleeding. Methods: In particular, we explore a convolutional neural network (CNN)-based deep learning framework to identify bleeding and non-bleeding CE images, where a pre-trained AlexNet neural network is used to train a transfer learning CNN that carries out the identification. Moreover, bleeding zones in a bleeding-identified image are also delineated using deep learning-based semantic segmentation that leverages a SegNet deep neural network. Results: To evaluate the performance of the proposed framework, we carry out experiments on two publicly available clinical datasets and achieve a 98.49% and 88.39% F1 score, respectively, on the capsule endoscopy.org and KID datasets. For bleeding zone identification, 94.42% global accuracy and 90.69% weighted intersection over union (IoU) are achieved. Conclusion: Finally, our performance results are compared to other recently developed state-of-the-art methods, and consistent performance advances are demonstrated in terms of performance measures for bleeding image and bleeding zone detection. Relative to the present and established practice of manual inspection and annotation of CE images by a physician, our framework enables considerable annotation time and human labor savings in bleeding detection in CE images, while providing the additional benefits of bleeding zone delineation and increased detection accuracy. Moreover, the overall cost of CE enabled by our framework will also be much lower due to the reduction of manual labor, which can make CE affordable for a larger population.

Deep Transfer Learning for Automated Intestinal Bleeding Detection in Capsule Endoscopy Imaging

Introduction: This review presents noteworthy advances in clinical and experimental Capsule Endoscopy (CE), focusing on the progress that has been reported over the last 5 years since our previous ...

Follow-up on: optimizing lesion detection in small bowel capsule endoscopy and beyond: from present problems to future solutions.

Capsule endoscopy (CE) is indicated as a first-line clinical examination for the detection of small-bowel pathology, and there is an ever-growing drive for it to become a method for the screening of the entire gastrointestinal tract (GI). Although CE's main function is diagnosis, the research for therapeutic capabilities has intensified to make therapeutic capsule endoscopy (TCE) a target within reach. This manuscript presents the research evolution of CE and TCE through the last 5 years and describes notable problems, as well as clinical and technological challenges to overcome. This review also reports the state-of-the-art of capsule devices with a focus on CE research prototypes promising an enhanced diagnostic yield (DY) and treatment. Lastly, this article provides an overview of the research progress made in software for enhancing DY by increasing the accuracy of abnormality detection and lesion localisation.

https://www.termedia.pl/Journal/-41/pdf-37508-10?filename=The future.pdf

The future of capsule endoscopy in clinical practice: from diagnostic to therapeutic experimental prototype capsules.

Gastrointestinal endoscopy has become a widely used technique to diagnose intestinal anomalies like polyp. However, the vast quantities of images produced in the detection process greatly increase the burden of clinicians and the misdiagnosis rate. To tackle this problem, a new computer-assisted detection system called Salient Codebook Locality-Constrained Linear Coding (SCLLC) with Annular Spatial Pyramid Matching (ASPM) is proposed in this paper to classify intestinal polyp images automatically. Firstly, SIFT features are extracted from images and k-means clustering method is utilized on the patch features to obtain the initial codebook. Secondly, the proposed SCLLC algorithm is employed to achieve the salient codebook and encode the features by emphasizing the salient basis vectors in feature coding process. Then, a max-pooling strategy of annular region segmentation based on Spatial Pyramid Matching is proposed to improve the effectiveness of processing for intestinal images. Finally, SVM classifier is developed to carry out polyp images classification tasks. The experimental results exhibit promising 94.10% accuracy, 91.20% sensitivity and 97.01% specificity. In addition, the 0.97 s detecting time indicates the feasibility of clinical application. Our proposed method can effectively improve the overall performance of intestinal polyp recognition by integrating the internal base vector correlation of codebook and considering the annular structure of the intestinal images.

Intestinal Polyp Recognition Based on Salient Codebook Locality-Constrained Linear Coding with Annular Spatial Pyramid Matching

This paper presents a method for detecting bleeding and tumors within capsule endoscopy (CE) images. Because CE can be used for visual, non-invasive examinations of the small bowel, it has recently become widely used. However, as a capsule progresses along the gastrointestinal tract, it collects vast quantities of images that make diagnosis a very time-consuming task. To address this problem, many computational approaches for anomaly detection have been proposed. Common to most approaches is the belief that color, texture, and shape are the most promising features for detecting anomalies within CE images. However, given that the requirements for each type of feature vary according to the anomaly, generally, it is essential to apply a complicated combination of techniques for multiple-feature extraction. In this study, in order to realize a scheme that covers the features of color, texture, and shape and can be applied to lesion areas of various sizes, a geometric image feature called local-contrast-enhanced higher-order local auto-correlation (LCE-HLAC) is proposed. Moreover, although the HSV color space is generally regarded as being appropriate for the analysis of CE images, imbalances in the distributions of utilized hue components limit discriminatory performance for normal and anomalous images. Accordingly, an image pre-processing method that uses a non-linear conversion model for the HSV color space is also proposed. Anomaly detection is implemented using a support vector machine classifier. The results of experiments, conducted with normal, bleeding, and tumor images obtained from 28 patients, demonstrate both the feasibility and superiority of the proposed method for both bleeding and tumor detection tasks.

Bleeding and Tumor Detection for Capsule Endoscopy Images Using Improved Geometric Feature

Optical colonoscopy is the most common approach to diagnosing bowel diseases through direct colon and rectum inspections. Periodic optical colonoscopy examinations are particularly important for detecting cancers at early stages while still treatable. However, diagnostic accuracy is highly dependent on both the experience and knowledge of the medical doctor. Moreover, it is extremely difficult, even for specialist doctors, to detect the early stages of cancer when obscured by inflammations of the colonic mucosa due to intractable inflammatory bowel diseases, such as ulcerative colitis. Thus, to assist the UC diagnosis, it is necessary to develop a new technology that can retrieve similar cases of diagnostic target image from cases in the past that stored the diagnosed images with various symptoms of colonic mucosa. In order to assist diagnoses with optical colonoscopy, this paper proposes a retrieval method for colonoscopy images that can cope with multiscale objects. The proposed method can retrieve similar colonoscopy images despite varying visible sizes of the target objects. Through three experiments conducted with real clinical colonoscopy images, we demonstrate that the method is able to retrieve objects of any visible size and any location at a high level of accuracy.

/pdf/image-retrieval-method-for-multiscale-objects-from-optical-29zu45qiht.pdf

Image Retrieval Method for Multiscale Objects from Optical Colonoscopy Images

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Bleeding and Tumor Detection for Capsule Endoscopy Images Using Improved Geometric Feature

Image Retrieval Method for Multiscale Objects from Optical Colonoscopy Images