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2022 Contribution to conference Open Access OPEN

Towards a GUI for declarative medical image analysis: cognitive and memory load issues
Broccia G., Ciancia V., Latella D., Massink M.
In medical imaging, (semi-)automatic image analysis techniques have been proposed to support the current time-consuming and cognitively demanding practice of manual segmentation of regions of interest (ROIs). The recently proposed image query language ImgQL, based on spatial logic and model checking, represents segmentation methods as concise, domain-oriented, human-readable procedures aimed at domain experts rather than technologists, and has been validated in several case studies. Such efforts are directed towards a human-centred Artificial Intelligence methodology. To this aim, we complemented the ongoing research line with a study of the Human-Computer Interaction aspects. In this work we investigate the design of a graphical user interface (GUI) prototype that supports the analysis procedure with minimal impact on the focus and the memory load of domain experts.Source: HCII 2022 - 24th International Conference on Human-Computer Interaction, pp. 103–111, Online conference, 26/06-01/07/2022
DOI: 10.1007/978-3-031-06388-6_14

See at: ISTI Repository Open Access | link.springer.com Restricted | CNR ExploRA Restricted


2021 Conference article Open Access OPEN

Feasibility of Spatial Model Checking for Nevus Segmentation
Belmonte G., Broccia G., Ciancia V., Latella D., Massink M.
Recently developed spatial and spatio-temporal model checking techniques have a wide range of application domains, among which large scale distributed systems as well as signal and image analysis. In the latter domain, automatic and semi-automatic contouring in Medical Imaging has shown to be a very promising and versatile application that may facilitate the work of professionals in this domain, while supporting explainability, easy replicability and exchange of medical image analysis methods. In recent work, spatial model-checking has been applied to the 3D contouring of brain tumours and related oedema in magnetic resonance images of the brain. In the present paper we address the contouring of 2D images of nevi. One of the challenges of contouring nevi is that they show considerable inhomogeneity in shape, colour, texture and size. In addition these images often include also extraneous elements such as hairs, patches and rulers. To deal with this challenge we explore the use of a texture similarity operator in combination with spatial logic operators. We investigate the feasibility of our technique on images of a large public database. We compare the results with associated ground truth segmentation provided by domain experts; the results are very promising, both from the quality and from the performance point of view.Source: FormaliSE: International Conference on Formal Methods in Software Engineering, pp. 1–12, 18-21/05/2021
DOI: 10.1109/formalise52586.2021.00007

See at: ISTI Repository Open Access | CNR ExploRA Restricted | www.computer.org Restricted


2021 Report Open Access OPEN

A graphical user interface for medical image analysis with declarative spatial logic - Cognitive and memory load evaluation
Broccia G., Ciancia V., Latella D., Massink M.
Logic based (semi-)automatic contouring in Medical Imaging has shown to be a very promising and versatile technique that can potentially greatly facilitate the work of different professionals in this domain while supporting explainability, easy replicability and exchange of medical image analysis methods. In such a context there is a clear need of a prototype Graphical User Interface (GUI) support for professionals which is usable, understandable and which reduces unnecessary cognitive load to the minimum, so that the focus of attention can remain on the main, critical, tasks such as image segmentation in support of planning of radiotherapy. In this paper we introduce a first proposal for a graphical user interface for the segmentation of medical images via the spatial logic based analyser VoxLogicA. Since both the logic approach to image analysis and its application in medical imaging are completely new, this is the first step in an iterative development process that will involve various analysis and development techniques, including empirical research and formal analysis. In the current work we analyse the GUI with a focus on the cognitive and memory load aspects which are critical in this domain of application.Source: ISTI Technical Report, ISTI-2021-TR/012, pp.1–39, 2021
DOI: 10.32079/isti-tr-2021/012

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2021 Conference article Embargo

Querying medical imaging datasets using spatial logics (Position paper)
Belmonte G., Broccia G., Bussi L., Ciancia V., Latella D., Massink M.
Nowadays a plethora of health data is available for clinical and research usage. Such existing datasets can be augmented through artificial-intelligence-based methods by automatic, personalised annotations and recommendations. This huge amount of data lends itself to new usage scenarios outside the boundaries where it was created; just to give some examples: to aggregate data sources in order to make research work more relevant; to incorporate a diversity of datasets in training of Machine Learning algorithms; to support expert decisions in telemedicine. In such a context, there is a growing need for a paradigm shift towards means to interrogate medical databases in a semantically meaningful way, fulfilling privacy and legal requirements, and transparently with respect to ethical concerns. In the specific domain of Medical Imaging, in this paper we sketch a research plan devoted to the definition and implementation of query languages that can unambiguously express semantically rich queries on possibly multi-dimensional images, in a human-readable, expert-friendly and concise way. Our approach is based on querying images using Topological Spatial Logics, building upon a novel spatial model checker called VoxLogicA, to execute such queries in a fully automated way.Source: MEDI 2021 - Advances in Model and Data Engineering in the Digitalization Era, pp. 285–301, Tallinn, Estonia, 21-23/06/2021
DOI: 10.1007/978-3-030-87657-9_22

See at: link.springer.com Restricted | link.springer.com Restricted | CNR ExploRA Restricted


2020 Journal article Open Access OPEN

Flexible automatic support for web accessibility validation
Broccia G., Manca M., Paternò F., Pulina F.
Automatic support for web accessibility validation needs to evolve for several reasons The increasingly recognised importance of accessibility implies that various stakeholders, with different expertise, look at it from different viewpoints and have different requirements regarding the types of outputs they expect. The technologies used to support Web application access are evolving along with the associated accessibility guidelines. We present a novel tool that aims to provide flexible and open support for addressing such issues. We describe the design of its main features including support for recent guidelines and tailored results presentations, and report on first technical and empirical validation s that have provided positive feedbackSource: Proceedings of the ACM on human-computer interaction 4 (2020). doi:10.1145/3397871
DOI: 10.1145/3397871

See at: ISTI Repository Open Access | Proceedings of the ACM on Human-Computer Interaction Restricted | Proceedings of the ACM on Human-Computer Interaction Restricted | Proceedings of the ACM on Human-Computer Interaction Restricted | Proceedings of the ACM on Human-Computer Interaction Restricted | CNR ExploRA Restricted


2020 Report Open Access OPEN

Using spatial logic and model checking for nevus segmentation
Belmonte G., Broccia G., Ciancia V., Latella D., Massink M.
Spatial and spatio-temporal model checking techniques have a wide range of application domains, among which large scale distributed systems and signal and image analysis. In the latter domain, automatic and semi-automatic contouring in Medical Imaging has shown to be a very promising and versatile application that can greatly facilitate the work of professionals in this domain, while supporting explainability, easy replicability and exchange of medical image analysis methods. In recent work we have applied this model-checking technique to the (3D) contouring of tumours and related oedema in magnetic resonance images of the brain. In the current work we address the contouring of (2D) images of nevi. One of the challenges of treating nevi images is their considerable inhomogeneity in shape, colour, texture and size. To deal with this challenge we use a texture similarity operator, in combination with spatial logic operators. We apply our technique on images of a large public database and compare the results with associated ground truth segmentation provided by domain experts.Source: ISTI Technical Reports 2020/017, 2020
DOI: 10.32079/isti-tr-2020/017

See at: ISTI Repository Open Access | CNR ExploRA Open Access