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2. RSNA-MICCAI Panel Discussion: Machine Learning for Radiology from Challenges to Clinical Applications.

Author

Mongan, John, Kalpathy-Cramer, Jayashree, Flanders, Adam, and George Linguraru, Marius

Subjects
Artificial Neural Network Algorithms, Back-Propagation, Machine Learning Algorithms
Abstract

On October 5, 2020, the Medical Image Computing and Computer Assisted Intervention Society (MICCAI) 2020 conference hosted a virtual panel discussion with members of the Machine Learning Steering Subcommittee of the Radiological Society of North America. The MICCAI Society brings together scientists, engineers, physicians, educators, and students from around the world. Both societies share a vision to develop radiologic and medical imaging techniques through advanced quantitative imaging biomarkers and artificial intelligence. The panel elaborated on how collaborations between radiologists and machine learning scientists facilitate the creation and clinical success of imaging technology for radiology. This report presents structured highlights of the moderated dialogue at the panel. Keywords: Back-Propagation, Artificial Neural Network Algorithms, Machine Learning Algorithms © RSNA, 2021.

Published
2021

3. Automated image-based colon cleansing for laxative-free CT colonography computer-aided polyp detection

Author

George Linguraru, Marius, Panjwani, Neil, G. Fletcher, Joel, and M. Summers, Ronald

Subjects
Radiation Therapy Physics, Radiographic Image Enhancement, Colon, Laxatives, Colonic Polyps, Humans, Radiographic Image Interpretation, Computer-Assisted, Reproducibility of Results, Colonography, Computed Tomographic, Sensitivity and Specificity, digestive system diseases, Algorithms, Pattern Recognition, Automated
Abstract

Purpose: To evaluate the performance of a computer-aided detection (CAD) system for detecting colonic polyps at noncathartic computed tomography colonography (CTC) in conjunction with an automated image-based colon cleansing algorithm.Methods: An automated colon cleansing algorithm was designed to detect and subtract tagged-stool, accounting for heterogeneity and poor tagging, to be used in conjunction with a colon CAD system. The method is locally adaptive and combines intensity, shape, and texture analysis with probabilistic optimization. CTC data from cathartic-free bowel preparation were acquired for testing and training the parameters. Patients underwent various colonic preparations with barium or Gastroview in divided doses over 48 h before scanning. No laxatives were administered and no dietary modifications were required. Cases were selected from a polyp-enriched cohort and included scans in which at least 90% of the solid stool was visually estimated to be tagged and each colonic segment was distended in either the prone or supine view. The CAD system was run comparatively with and without the stool subtraction algorithm.Results: The dataset comprised 38 CTC scans from prone and/or supine scans of 19 patients containing 44 polyps larger than 10 mm (22 unique polyps, if matched between prone and supine scans). The results are robust on fine details around folds, thin-stool linings on the colonic wall, near polyps and in large fluid/stool pools. The sensitivity of the CAD system is 70.5% per polyp at a rate of 5.75 false positives/scan without using the stool subtraction module. This detection improved significantly (p = 0.009) after automated colon cleansing on cathartic-free data to 86.4% true positive rate at 5.75 false positives/scan.Conclusions: An automated image-based colon cleansing algorithm designed to overcome the challenges of the noncathartic colon significantly improves the sensitivity of colon CAD by approximately 15%.

Published
2011

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