Dataset Information

Automated frame selection process for high-resolution microendoscopy.

ABSTRACT: We developed an automated frame selection algorithm for high-resolution microendoscopy video sequences. The algorithm rapidly selects a representative frame with minimal motion artifact from a short video sequence, enabling fully automated image analysis at the point-of-care. The algorithm was evaluated by quantitative comparison of diagnostically relevant image features and diagnostic classification results obtained using automated frame selection versus manual frame selection. A data set consisting of video sequences collected in vivo from 100 oral sites and 167 esophageal sites was used in the analysis. The area under the receiver operating characteristic curve was 0.78 (automated selection) versus 0.82 (manual selection) for oral sites, and 0.93 (automated selection) versus 0.92 (manual selection) for esophageal sites. The implementation of fully automated high-resolution microendoscopy at the point-of-care has the potential to reduce the number of biopsies needed for accurate diagnosis of precancer and cancer in low-resource settings where there may be limited infrastructure and personnel for standard histologic analysis.

SUBMITTER: Ishijima A

PROVIDER: S-EPMC4412137 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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Automated frame selection process for high-resolution microendoscopy.

Ishijima Ayumu A Schwarz Richard A RA Shin Dongsuk D Mondrik Sharon S Vigneswaran Nadarajah N Gillenwater Ann M AM Anandasabapathy Sharmila S Richards-Kortum Rebecca R

Journal of biomedical optics 20150401 4

We developed an automated frame selection algorithm for high-resolution microendoscopy video sequences. The algorithm rapidly selects a representative frame with minimal motion artifact from a short video sequence, enabling fully automated image analysis at the point-of-care. The algorithm was evaluated by quantitative comparison of diagnostically relevant image features and diagnostic classification results obtained using automated frame selection versus manual frame selection. A data set consi ...[more]

PMID: 25919426

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Project description:EUS-guided FNA is one of the few techniques that can obtain cells and tissue from the liver and pancreas. However, the technique remains vulnerable to poor specimen quality and sampling error.To evaluate the ability of a high-resolution microendoscope (HRME) to visualize the cellular and architectural features of normal and malignant liver and pancreatic tissue ex vivo, to assess the ability of endosonographers to identify normal and neoplastic tissue by using HRME images, and to demonstrate preliminary technical feasibility of in vivo HRME imaging via EUS fine-needle puncture (FNP).Ex vivo pilot feasibility study in human tissue; in vivo swine model.Two academic medical centers.Co-registered HRME images and biopsies were obtained from surgically resected hepatic and pancreatic tissues from 44 patients.Images were divided into training (12 images) and test (80 images) sets containing a range of normal and pathologic conditions for each organ. After viewing the training sets, 9 endosonographers attempted to distinguish malignant tissue from normal or benign lesions in the test sets, each of which contained 40 unique images with individual diagnoses from pathology.Image acquisition feasibility, ex vivo and in vivo. Ability of endosonographers to recognize features of normal/benign or malignant tissue from the liver and pancreas.Overall, the 9 endosonographers achieved median accuracy figures of 85% in the liver and 90% in the pancreas. The endosonographers with prior experience in reading HRME images achieved accuracy rates between 90% and 95%. Technical feasibility of HRME imaging through a 19-gauge EUS-FNP needle was demonstrated in an in vivo swine model.Ex vivo study.High-resolution microendoscopy allows real-time imaging of cellular-level morphology and tissue architecture in the liver and pancreas. The technique appears to have a short learning curve, after which endosonographers achieved high accuracy rates in distinguishing malignant tissue from normal and benign pathology in both organs. Translating this imaging platform to the in vivo setting appears technically feasible.

Dataset Information

Automated frame selection process for high-resolution microendoscopy.

Publications

Automated frame selection process for high-resolution microendoscopy.

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