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2. Advent of GAN: How does a generative AI create a moodboard?

Author

Holmlid, S., Rodrigues, V., Westin, C., Krogh, P. G., Mäkelä, M., Svanaes, D., Wikberg-Nilsson, Å., Mikkonen, Jussi Ville, Holmlid, S., Rodrigues, V., Westin, C., Krogh, P. G., Mäkelä, M., Svanaes, D., Wikberg-Nilsson, Å., and Mikkonen, Jussi Ville

Abstract

The human creativity is now being supported, or challenged, by an easy access to generative tools utilising Artificial Intelligence (AI). Within the last few years, Generative Adversarial Network-based image generation tools have become available for the public. In this paper, we explore how one such tool, called Midjourney, could be used for generating moodboards. Our work focuses on exploring moodboard image generation via text-based prompts. We take note of the active online discussions, where examples and ethical concerns have strongly emerged during the late 2022. As a result, the AI produced imagery that the authors could not have produced themselves in any reasonable amount of time, if at all. The GAN-generated images are of high quality, and visually useable for design purposes. However, there are ethical concerns on the sources of the images used for training the AI systems, and copyright concerns on the outcomes.

Published
2023

3. Learning for Air Traffic Management: guidelines for future AI systems

Author

Cocchioni, Matteo (author), Bonelli, Stefano (author), Westin, C. A. L. (author), Borst, C. (author), Hilburn, B (author), Cocchioni, Matteo (author), Bonelli, Stefano (author), Westin, C. A. L. (author), Borst, C. (author), and Hilburn, B (author)

Abstract

The SESAR-funded Modern ATM via Human / Automation Learning Optimisation (MAHALO) project recently completed two years of technical work exploring the human performance impacts of AI and Machine Learning (ML), as applied to enroute ATC conflict detection and resolution (CD&R). It first developed a hybrid ML CD&R capability, along with a realtime simulation platform and experimental User Interface. After a series of development trials, the project culminated in a pair of field studies (i.e., human-in-the-loop trials) across two EU countries, with a total of 35 operational air traffic controllers. In each of these two field studies, controller behaviour was first captured in a pre-test phase, and used to train the ML system. Subsequent main experiment trials then experimentally manipulated within controllers both Conformance (as either a personalised-, group average-, or optimized model) and Transparency (as ether a baseline vector depiction, an enhanced graphical diagram, or a diagram-plus-text presentation). The proposed paper presents guidelines on the design and implementation of ML systems in Air Traffic Control, derived from the results and lesson learned from the Simulations, as well as the qualitative feedback received from the controllers themselves., Control & Simulation

Published
2023
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4. Diffusion Propagator Estimation Using Radial Basis Functions

Author

Rathi, Yogesh, Niethammer, Marc, Laun, Frederik, Setsompop, Kawin, Michailovich, Oleg, Grant, P. Ellen, Westin, C.-F., Farin, Gerald, Series editor, Hege, Hans-Christian, Series editor, Hoffman, David, Series editor, Johnson, Christopher R., Series editor, Polthier, Konrad, Series editor, Rumpf, Martin, Series editor, Schultz, Thomas, editor, Nedjati-Gilani, Gemma, editor, Venkataraman, Archana, editor, O'Donnell, Lauren, editor, and Panagiotaki, Eleftheria, editor

Published
2014
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5. Comparing Simultaneous Multi-slice Diffusion Acquisitions

Author

Rathi, Yogesh, Gagoski, Borjan, Setsompop, Kawin, Grant, P. Ellen, Westin, C.-F., Farin, Gerald, Series editor, Hege, Hans-Christian, Series editor, Hoffman, David, Series editor, Johnson, Christopher R., Series editor, Polthier, Konrad, Series editor, Rumpf, Martin, Series editor, Schultz, Thomas, editor, Nedjati-Gilani, Gemma, editor, Venkataraman, Archana, editor, O'Donnell, Lauren, editor, and Panagiotaki, Eleftheria, editor

Published
2014
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6. Sparse Multi-Shell Diffusion Imaging

Author

Rathi, Yogesh, Michailovich, O., Setsompop, K., Bouix, S., Shenton, M. E., Westin, C. -F., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Fichtinger, Gabor, editor, Martel, Anne, editor, and Peters, Terry, editor

Published
2011
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8. Signal LMMSE Estimation from Multiple Samples in MRI and DT-MRI

Author

Aja-Fernández, S., Alberola-López, C., Westin, C. -F., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ayache, Nicholas, editor, Ourselin, Sébastien, editor, and Maeder, Anthony, editor

Published
2007
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11. Geostatistical Medical Image Registration

Author

Ruiz-Alzola, J., Suarez, E., Alberola-Lopez, C., Warfield, S. K., Westin, C. -F., Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Ellis, Randy E., editor, and Peters, Terry M., editor

Published
2003
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24. Contributors

Author

Alexander, Daniel C., primary, Andersson, Jesper L.R., additional, Assaf, Yaniv, additional, Ball, Gareth, additional, Bartsch, Andreas J., additional, Basser, Peter J., additional, Beaulieu, Christian, additional, Behrens, Timothy E.J., additional, Biller, Armin, additional, Bodini, Benedetta, additional, Boorman, Erie D., additional, Budisavljević, Sanja, additional, Catani, Marco, additional, Chen, Charles, additional, Ciccarelli, Olga, additional, Coalson, Tim, additional, Cohen, Yoram, additional, Counsell, Serena J., additional, Dikranian, Krikor, additional, Edgar, Julia M., additional, David Edwards, A., additional, Glasser, Matthew F., additional, Griffiths, Ian R., additional, Harwell, John, additional, Homola, György A., additional, Hubbard, Penny L., additional, Jbabdi, Saad, additional, Johansen-Berg, Heidi, additional, Jones, Derek K., additional, Kindlmann, Gordon, additional, Klein, Johannes C., additional, Kubicki, M., additional, Lanciego, José L., additional, Mars, Rogier B., additional, Miller, Karla L., additional, Morecraft, Robert J., additional, Özarslan, Evren, additional, Pandit, Anand, additional, Pandya, Deepak N., additional, Parker, Geoffrey J.M., additional, Pasternak, O., additional, Pipe, Jim, additional, Rushworth, Matthew F.S., additional, Salat, David H., additional, Sallet, Jérôme, additional, Scholz, Jan, additional, Seunarine, Kiran K., additional, Shenton, M.E., additional, Smith, Stephen M., additional, Sotiropoulos, Stamatios N., additional, Sporns, Olaf, additional, Tomassini, Valentina, additional, Ugolini, Gabriella, additional, Van Essen, David C., additional, Westin, C-F., additional, and Wouterlood, Floris G., additional

Published
2014
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25. Joint Modeling of Anatomical and Functional Connectivity for Population Studies

Author

Venkataraman, A, Rathi, Y, Kubicki, M, Westin, C, Golland, P, Venkataraman, A, Rathi, Y, Kubicki, M, Westin, C, and Golland, P

Abstract

We propose a novel probabilistic framework to merge information from diffusion weighted imaging tractography and resting-state functional magnetic resonance imaging correlations to identify connectivity patterns in the brain. In particular, we model the interaction between latent anatomical and functional connectivity and present an intuitive extension to population studies. We employ the EM algorithm to estimate the model parameters by maximizing the data likelihood. The method simultaneously infers the templates of latent connectivity for each population and the differences in connectivity between the groups. We demonstrate our method on a schizophrenia study. Our model identifies significant increases in functional connectivity between the parietal/posterior cingulate region and the frontal lobe and reduced functional connectivity between the parietal/posterior cingulate region and the temporal lobe in schizophrenia. We further establish that our model learns predictive differences between the control and clinical populations, and that combining the two modalities yields better results than considering each one in isolation. © 2011 IEEE.

Published
2021

30. The impact of gradient non-linearity on Maxwell compensation when using asymmetric gradient waveforms for tensor-valued diffusion encoding

Author

Szczepankiewicz, F., Eichner, C., Anwander, A., Westin, C., and Paquette, M.

Abstract

Gradient non-linearity distorts the shape of the gradient waveform used for diffuison encoding. This distortion also compromises Maxwell compensation in asymmetric gradient waveforms. We show that one of two strategies for Maxwell compensation fails under non-linear gradiets (K-nulling) whereas M-nulling is immune to this effect.

Published
2020

31. SlicerDMRI: Diffusion MRI and Tractography Research Software for Brain Cancer Surgery Planning and Visualization

Author

Zhang, F., Noh, T., Juvekar, P., Frisken, S.F., Rigolo, L., Norton, I., Kapur, T., Pujol, S., Wells, W., Yarmarkovich, A., Kindlmann, G., Wassermann, D., Jose Estepar, R.S., Rathi, Y., Kikinis, R., Johnson, H.J., Westin, C.-F., Pieper, S., Golby, A.J., O'Donnell, L.J., and Publica

Abstract

PURPOSE We present SlicerDMRI, an open-source software suite that enables research using diffusion magnetic resonance imaging (dMRI), the only modality that can map the white matter connections of the living human brain. SlicerDMRI enables analysis and visualization of dMRI data and is aimed at the needs of clinical research users. SlicerDMRI is built upon and deeply integrated with 3D Slicer, a National Institutes of Health-supported open-source platform for medical image informatics, image processing, and three-dimensional visualization. Integration with 3D Slicer provides many features of interest to cancer researchers, such as real-time integration with neuronavigation equipment, intraoperative imaging modalities, and multimodal data fusion. One key application of SlicerDMRI is in neurosurgery research, where brain mapping using dMRI can provide patient-specific maps of critical brain connections as well as insight into the tissue microstructure that surrounds brain tumors. PATIENTS AND METHODS In this article, we focus on a demonstration of SlicerDMRI as an informatics tool to enable end-to-end dMRI analyses in two retrospective imaging data sets from patients with high-grade glioma. Analyses demonstrated here include conventional diffusion tensor analysis, advanced multifiber tractography, automated identification of critical fiber tracts, and integration of multimodal imagery with dMRI. RESULTS We illustrate the ability of SlicerDMRI to perform both conventional and advanced dMRI analyses as well as to enable multimodal image analysis and visualization. We provide an overview of the clinical rationale for each analysis along with pointers to the SlicerDMRI tools used in each. CONCLUSION SlicerDMRI provides open-source and clinician-accessible research software tools for dMRI analysis. SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager.

Published
2020

34. Homomorphic Filtering of DT-MRI Fields

Author

Castaño Moraga, C. A., Westin, C. -F., Ruiz-Alzola, J., Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Ellis, Randy E., editor, and Peters, Terry M., editor

Published
2003
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44. Design and Evaluation Study of Visual Analytics Decision Support Tools in Air Traffic Control.

Author

Zohrevandi, E., Westin, C. A. L., Lundberg, J., and Ynnerman, A.

Subjects
*AIR traffic control, *VISUAL analytics, *CONFLICT management, *EXPERIMENTAL design, *VISUALIZATION
Abstract

Operators in air traffic control facing time‐ and safety‐critical situations call for efficient, reliable and robust real‐time processing and interpretation of complex data. Automation support tools aid controllers in these processes to prevent separation losses between aircraft. Issues of current support tools include limited 'what‐if' and 'what‐else' probe functionalities in relation to vertical solutions. This work presents the design and evaluation of two visual analytics interfaces that promote contextual awareness and support 'what‐if' and 'what‐else' probes in the spatio‐temporal domain aiming to improve information integration and support controllers in prioritising conflict resolution. Both interfaces visualize vertical solution spaces against a time‐altitude graph. The main contributions of this paper are: (a) the presentation of two interfaces for supporting conflict solving; (b) the novel representation of how vertical information and aircraft rate of climb and descent affect conflicts and (c) an evaluation and comparison of the interfaces with a traditional air traffic control support system. The evaluation study was performed with domain experts to compare the effects of visualization concepts on operator engagement in processing solutions suggested by the tools. Results show that the visualizations support operators' ability to understand and resolve conflicts. Based on the results, general design guidelines for time‐critical domains are proposed. [ABSTRACT FROM AUTHOR]

Published
2022
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