"Semi-Automated Contour Recognition using DICOMautomaton" @ ICCR 2013
Channel: haldclark
Date: 28/10/2014

Video footage of a presentation I (Hal Clark) gave at the International Conference on the use of Computers in Radiation Therapy (ICCR) in Melbourne on 20130509 (May 9th, 2013). Note that the audio recorded was nearly inaudible and so an attempt was made to re-record much of the presentation afterwards.

An accompanying paper was published and can be accessed at http://iopscience.iop.org/1742-6596/489/1/012088/ or http://dx.doi.org/10.1088/1742-6596/489/1/012088 .

Abstract:
Purpose: A system has been developed which recognizes and classifies Digital Imaging and Communication in Medicine contour data with minimal human intervention. It allows researchers to overcome obstacles which tax analysis and mining systems, including inconsistent naming conventions and differences in data age or resolution. Methods: Lexicographic and geometric analysis is used for recognition. Well-known lexicographic methods implemented include Levenshtein-Damerau, bag-of-characters, Double Metaphone, Soundex, and (word and character)-N-grams. Geometrical implementations include 3D Fourier Descriptors, probability spheres, boolean overlap, simple feature comparison (e.g. eccentricity, volume) and rule-based techniques. Both analyses implement custom, domain-specific modules (e.g. emphasis differentiating left/right organ variants). Contour labels from 60 head and neck patients are used for cross-validation. Results: Mixed-lexicographical methods show an effective improvement in more than 10% of recognition attempts compared with a pure Levenshtein-Damerau approach when withholding 70% of the lexicon. Domain-specific and geometrical techniques further boost performance. Conclusions: DICOMautomaton allows users to recognize contours semi-automatically. As usage increases and the lexicon is filled with additional structures, performance improves, increasing the overall utility of the system.