A supervised learning approach for Crohn's disease detection using higher-order image statistics and a novel shape asymmetry measure.

Increasing incidence of Crohn's disease (CD) in the Western world has made its accurate diagnosis an important medical challenge. The current reference standard for diagnosis, colonoscopy, is time-consuming and invasive while magnetic resonance imaging (MRI) has emerged as the preferred noninvasive procedure over colonoscopy. Current MRI approaches assess rate of contrast enhancement and bowel wall thickness, and rely on extensive manual segmentation for accurate analysis. We propose a supervised learning method for the identification and localization of regions in abdominal magnetic resonance images that have been affected by CD. Low-level features like intensity and texture are used with shape asymmetry information to distinguish between diseased and normal regions. Particular emphasis is laid on a novel entropy-based shape asymmetry method and higher-order statistics like skewness and kurtosis. Multi-scale feature extraction renders the method robust. Experiments on real patient data show that our features achieve a high level of accuracy and perform better than two competing methods.

Digital Pathology Operations at an NYC Tertiary Cancer Center During the First 4 Months of COVID-19 Pandemic Response.

Implementation of an infrastructure to support digital pathology began in 2006 at Memorial Sloan Kettering Cancer Center. The public health emergency and COVID-19 pandemic regulations in New York City required a novel workflow to sustain existing operations. While regulatory enforcement discretions offered faculty workspace flexibility, a substantial portion of laboratory and digital pathology workflows require on-site presence of staff. Maintaining social distancing and offering staggered work schedules. Due to a decrease in patients seeking health care at the onset of the pandemic, a temporary decrease in patient specimens was observed. Hospital and travel regulations impacted onsite vendor technical support. Digital glass slide scanning activities onsite proceeded without interruption throughout the pandemic, with challenges including staff who required quarantine due to virus exposure, unrelated illness, family support, or lack of public transportation. During the public health emergency, we validated digital pathology systems for a remote pathology operation. Since March 2020, the departmental digital pathology staff were able to maintain scanning volumes of over 100 000 slides per month. The digital scanning team reprioritized archival slide scanning and participated in a remote sign-out validation and successful submission of New York State approval for a laboratory developed test. Digital pathology offers a health care delivery model where pathologists can perform their sign out duties at remote location and prevent disruptions to critical pathology services for patients seeking care at our institution during emergencies. Development of standard operating procedures to support digital workflows will maintain turnaround times and enable clinical operations during emergency or otherwise unanticipated events.

Computational modeling for assessment of IBD: to be or not to be?

The grading of inflammatory bowel disease (IBD) severity is important to determine the proper treatment strategy and to quantify the response to treatment. Traditionally, ileocolonoscopy is considered the reference standard for assessment of IBD. However, the procedure is invasive and requires extensive bowel preparation. Magnetic resonance imaging (MRI) has become an important tool for determining the presence of disease activity. Unfortunately, only moderate interobserver agreement is reported for most of the radiological severity measures. There is a clear demand for automated evaluation of MR images in Crohn's disease (CD). This paper aims to introduce a preliminary suite of fundamental tools for assessment of CD severity. It involves procedures for image analysis, classification and visualization to predict the colonoscopy disease scores.