A Visual Analytics Approach to Study Anatomic Covariation

Abstract

Gaining insight into anatomic covariation helps the understanding of organismic shape variability in general and is of particular interest for delimiting morphological modules. Generation of hypotheses on structural covariation is undoubtedly a highly creative process, and as such, requires an exploratory approach. In this work we propose a new local anatomic covariance tensor which enables interactive visualizations to explore covariation at different levels of detail, stimulating rapid formation and (qualitative) evaluation of hypotheses. The effectiveness of the presented approach is demonstrated on a muCT dataset of mouse mandibles for which results from the literature are successfully reproduced, while providing a more detailed representation of covariation compared to state-of-the-art methods

VAPOR: Visual Analytics for the Exploration of Pelvic Organ Variability in Radiotherapy

“…In shape space analysis, Hermann et al [27,28,29] investigate anatomic covariances in ensembles of data, providing also a state of the art report with prospects on the visual analysis of shapes [30]. Busking et al [31] propose to use a 2D scatter plot to represent the distribution of elements inside a cohort and to synthesize additional arbitrary objects in the shape space.…”

PREVIS: Predictive visual analytics of anatomical variability for radiotherapy decision support

“…In shape space analysis , Hermann et al [10,11] investigate anatomic covariances in ensembles of data, providing also a state of the art report with prospects on the visual analysis of shapes. Busking et al [12] propose a 2D scatter plot to represent the distribution of elements inside a cohort and to synthesize additional arbitrary objects in the shape space.…”

Visual Analysis of Tumor Control Models for Prediction of Radiotherapy Response

“…In many cases, patients are not analyzed individually. Previous work in cohort visualization mainly focuses on the comparative analysis of shape variability [BBP10, SPA*14, HSSK14]. Recently, Steenwijk et al [SMB*10], Zhang et al [ZGP14] and Klemm et al [KLR*13, KOJL*14] proposed interactive visual analysis of cohorts that goes also beyond shape analysis.…”