Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models

Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer's disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating high-throughput analysis of normal anatomy and pathology in large-scale studies of volumetric imaging

GEANT4 : a simulation toolkit

Abstract Geant4 is a toolkit for simulating the passage of particles through matter. It includes a complete range of functionality including tracking, geometry, physics models and hits. The physics processes offered cover a comprehensive range, including electromagnetic, hadronic and optical processes, a large set of long-lived particles, materials and elements, over a wide energy range starting, in some cases, from 250 eV and extending in others to the TeV energy range. It has been designed and constructed to expose the physics models utilised, to handle complex geometries, and to enable its easy adaptation for optimal use in different sets of applications. The toolkit is the result of a worldwide collaboration of physicists and software engineers. It has been created exploiting software engineering and object-oriented technology and implemented in the C++ programming language. It has been used in applications in particle physics, nuclear physics, accelerator design, space engineering and medical physics. PACS: 07.05.Tp; 13; 2

Internet enabled modelling of extended manufacturing enterprises using the process based techniques

The paper presents the preliminary results of an ongoing research project on Internet enabled process-based modelling of extended manufacturing enterprises. It is proposed to apply the Open System Architecture for CIM (CIMOSA) modelling framework alongside with object-oriented Petri Net models of enterprise processes and object-oriented techniques for extended enterprises modelling. The main features of the proposed approach are described and some components discussed. Elementary examples of object-oriented Petri Net implementation and real-time visualisation are presented

P ORTOLAN: a Model-Driven Cartography Framework

Processing large amounts of data to extract useful information is an essential task within companies. To help in this task, visualization techniques have been commonly used due to their capacity to present data in synthesized views, easier to understand and manage. However, achieving the right visualization display for a data set is a complex cartography process that involves several transformation steps to adapt the (domain) data to the (visualization) data format expected by visualization tools. To maximize the benefits of visualization we propose Portolan, a generic model-driven cartography framework that facilitates the discovery of the data to visualize, the specification of view definitions for that data and the transformations to bridge the gap with the visualization tools. Our approach has been implemented on top of the Eclipse EMF modeling framework and validated on three different use cases

Engineering simulations for cancer systems biology

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions

Requirements for Topology in 3D GIS

Topology and its various benefits are well understood within the context of 2D Geographical Information Systems. However, requirements in three-dimensional (3D) applications have yet to be defined, with factors such as lack of users' familiarity with the potential of such systems impeding this process. In this paper, we identify and review a number of requirements for topology in 3D applications. The review utilises existing topological frameworks and data models as a starting point. Three key areas were studied for the purposes of requirements identification, namely existing 2D topological systems, requirements for visualisation in 3D and requirements for 3D analysis supported by topology. This was followed by analysis of application areas such as earth sciences and urban modelling which are traditionally associated with GIS, as well as others including medical, biological and chemical science. Requirements for topological functionality in 3D were then grouped and categorised. The paper concludes by suggesting that these requirements can be used as a basis for the implementation of topology in 3D. It is the aim of this review to serve as a focus for further discussion and identification of additional applications that would benefit from 3D topology. © 2006 The Authors. Journal compilation © 2006 Blackwell Publishing Ltd

Explainable anatomical shape analysis through deep hierarchical generative models.

Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer's disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating highthroughput analysis of normal anatomy and pathology in largescale studies of volumetric imaging

Towards an automatic data value analysis method for relational databases

Data is becoming one of the world’s most valuable resources and it is suggested that those who own the data will own the future. However, despite data being an important asset, data owners struggle to assess its value. Some recent pioneer works have led to an increased awareness of the necessity for measuring data value. They have also put forward some simple but engaging survey-based methods to help with the first-level data assessment in an organisation. However, these methods are manual and they depend on the costly input of domain experts. In this paper, we propose to extend the manual survey-based approaches with additional metrics and dimensions derived from the evolving literature on data value dimensions and tailored specifically for our use case study. We also developed an automatic, metric-based data value assessment approach that (i) automatically quantifies the business value of data in Relational Databases (RDB), and (ii) provides a scoring method that facilitates the ranking and extraction of the most valuable RDB tables. We evaluate our proposed approach on a real-world RDB database from a small online retailer (MyVolts) and show in our experimental study that the data value assessments made by our automated system match those expressed by the domain expert approach

Process Performance Analysis in Large-Scale Systems Integrating Different Sources of Information

Process auditing using historical data can identify causes for poor performance and reveal opportunities to improve process operation. To date, the data used has been limited to process measurements; however other sources hold complementary information about the process behavior. This paper proposes a new approach to root-cause diagnosis, which also takes advantage of the information in utility, mechanical and electrical data, alarms and diagrams. Its benefit is demonstrated in an industrial case study, by tackling an important challenge in root-cause analysis: large-scale systems. This paper also defines specifications for a semi-automated tool to implement the proposed approach. © 2012 IFAC