E.V. Tsianos

A methodology for automated CPA extraction using liver biopsy image analysis and machine learning techniques

BACKGROUND AND OBJECTIVE Collagen proportional area (CPA) extraction in liver biopsy images provides the degree of fibrosis expansion in liver tissue, which is the most characteristic histological alteration in hepatitis C virus (HCV). Assessment of the fibrotic tissue is currently based on semiquantitative staging scores such as Ishak and Metavir. Since its introduction as a fibrotic tissue assessment technique, CPA calculation based on image analysis techniques has proven to be more accurate than semiquantitative scores. However, CPA has yet to reach everyday clinical practice, since the lack of standardized and robust methods for computerized image analysis for CPA assessment have proven to be a major limitation. METHODS The current work introduces a three-stage fully automated methodology for CPA extraction based on machine learning techniques. Specifically, clustering algorithms have been employed for background-tissue separation, as well as for fibrosis detection in liver tissue regions, in the first and the third stage of the methodology, respectively. Due to the existence of several types of tissue regions in the image (such as blood clots, muscle tissue, structural collagen, etc.), classification algorithms have been employed to identify liver tissue regions and exclude all other non-liver tissue regions from CPA computation. RESULTS For the evaluation of the methodology, 79 liver biopsy images have been employed, obtaining 1.31% mean absolute CPA error, with 0.923 concordance correlation coefficient. CONCLUSIONS The proposed methodology is designed to (i) avoid manual threshold-based and region selection processes, widely used in similar approaches presented in the literature, and (ii) minimize CPA calculation time.

A clustering based method for collagen proportional area extraction in liver biopsy images.

Collagen Proportional Area (CPA) extraction using digital image analysis (DIA) in liver biopsies provides an effective way to estimate the liver disease staging. CPA represents accurately fibrosis expansion in liver tissue. This paper presents an automated clustering-based method for fibrosis detection and CPA computation. Initially, a k-means based approach is employed to detect the liver tissue and eliminate the background. Next, the method decides about the adequacy of current biopsy, according to the size of liver tissue. Biopsies which contain small and segmented specimens must be repeated. Since the tissue has been detected, fibrosis areas are also found in the tissue. Finally, CPA is computed. For the evaluation of the proposed method 25 images are employed and the percentage errors of CPA are computed for each image. In the majority of the cases, small variation of CPA is computed, comparing to the experts annotation.

Clinical Use of Patency Capsule: A Comprehensive Review of the Literature

The patency capsule is a radiopaque, dissolvable diagnostic tool, similar in shape and size to small bowel capsule endoscopes. It was developed to offer a simple, safe, efficient, and accurate evaluation of small bowel functional patency. Although unable to provide direct visual information regarding the presence and location of strictures, masses, or luminal narrowing of the small bowel, a successful patency test minimizes the risk of retention and allows the safe administration of a capsule endoscope. However, its use entails a low risk of potentially harmful adverse events, which in their majority are indolent and resolve spontaneously. Abdominal pain and symptomatic retention are accountable for the majority of reported adverse events, whereas a limited number of reports describe life-threatening complications, namely intestinal obstruction, perforation, and intestinal ischemia. Computed tomography is the modality of choice for the identification of the exact position of an impacted patency capsule, whilst the use of plain abdominal radiographs should be avoided for the evaluation of the patency capsule position, as they provide false information. Hereby, we present a comprehensive review of the available literature regarding the characteristics, indications, clinical use, effectiveness, and adverse events of the patency capsule.10.1093/ibd/izy152_video1izy152.video15777752348001.