AnaFe: Visual Analytics of Image-derived Temporal Features – Focusing on the Spleen

I. Gutenko, K. Dmitriev, A. E. Kaufman, M. A. Barish

IEEE Transactions on Visualization and Computer Graphics

doi:10.1109/TVCG.2016.2598463

Abstract

We present a novel visualization framework, AnaFe, targeted at observing changes in the spleen over time through multiple image-derived features. Accurate monitoring of progressive changes is crucial for diseases that result in enlargement of the organ. Our system is comprised of multiple linked views combining visualization of temporal 3D organ data, related measurements, and features. Thus it enables the observation of progression and allows for simultaneous comparison within and between the subjects. AnaFe offers insights into the overall distribution of robustly extracted and reproducible quantitative imaging features and their changes within the population, and also enables detailed analysis of individual cases. It performs similarity comparison of temporal series of one subject to all other series in both sick and healthy groups. We demonstrate our system through two use case scenarios on a population of 189 spleen datasets from 68 subjects with various conditions observed over time.

Keywords:

Visual Knowledge Discovery, Temporal Feature Analysis, Radiomics

Technologies:

Node.js, Sails.js, AngularJS, Highcharts.js, three.js, scikit

Credits

Konstantin Dmitriev, M. A. Barish