Paper accepted for presentation at MIDL 2022

Our paper on nucleus segmentation with local rotation invariance has been accepted for poster presentation at the Medical Imaging with Deep Learning (MIDL) conference 2022. As one of the first in-person conferences (hybrid) in our field, MIDL, held in Zürich, 6 – 8 July 2022, will be a great opportunity to catch up with researchers in our field and discuss our work.

Illustration of segmentation predictions robustness with respect to input orientation. The red color map indicates the average pixel-wise difference. which is averaged across the six pairs of 90◦ rotations

Full paper “Multi-Organ Nucleus Segmentation Using a Locally Rotation Invariant Bispectrum U-Net“, V. Oreiller, J. Fageot, V. Andrearczyk, J. O. Prior and A. Depeursinge, available online: https://openreview.net/forum?id=paGzvj2t_x

Article on feature stability published in Nature Scientific Reports

Our work on Assessing radiomics feature stability with simulated CT acquisitions has been published in Nature Scientific Reports. The authors K. Flouris, O. Jimenez-del-Toro, C. Aberle, M. Bach, R. Schaer, M. M. Obmann, B. Stieltjes, H. Müller, A. Depeursinge and E. Konukoglu developed and validated a Computed Tomography (CT) simulator and showed that the variability, stability and discriminative power of the radiomics features extracted from the virtual phantom images are similar to those observed in a tandem phantom study. They also demonstrated that the simulator can be utilised to assess radiomics features’ stability and discriminative power.

More details in the article available online: https://www.nature.com/articles/s41598-022-08301-1

Annotated regions of interest on the anthropomorphic phantom.

HECKTOR 2021 proceedings available online

The proceedings of the HEad and neCK TumOR segmentation and outcome prediction in PET/CT images (HECKTOR) 2021 challenge, at MICCAI, are now available in a Springer LNCS book.

https://link.springer.com/book/10.1007/978-3-030-98253-9

The participants papers describe the various innovative methods, and our overview paper provides a description of the challenge, the data, the methods and the results.

Stay tuned for the next edition of the challenge: HECKTOR 2022 !

Paper published in Clinical and Translational Radiation Oncology

In our paper “Cleaning Radiotherapy Contours for Radiomics Studies, is it Worth it? A Head and Neck Cancer Study“, published in ctRO (Elsevier), we study the benefit of delineating tumor contours specifically for radiomics analyses (automatic prognostic prediction of patients with Head and Neck cancer). The contours originating from radiotherapy often include parts of surrounding organs (e.g. trachea, bones) which impact the extraction of visual features that characterize the tumor.

Example of VOI delineation: Radiotherapy (green), Resegmented (purple), and Dedicated (blue) overlayed on a fused FDG-PET/CT image. The blue contour is closer to the true volume of the primary tumor.

SwissNeuroRehab, a new model of neurorehabilitation. FlagShip project funded by Innosuisse

In 2021, the Swiss innovation agency Innosuisse has launched the funding scheme Flagship aimed at stimulating systemic innovation and transdisciplinary collaboration to solve future challenges relevant for the Swiss economy and society. Among the 15 supported projects out of 84 submitted, Innosuisse has financed with 11.2 MCHF over 5 years the project SwissNeuroRehab.

As part of a large Swiss consortium working on this project, our group at the University of Applied Science of Western Switzerland (HES-SO) will lead the “Data” sub project.


SwissNeuroRehab aims at developing a novel model of neurorehabilitation along the continuum of care, from the hospital to home. In the first phase, the project will focus on stroke, traumatic brain injury and spinal cord injury. Through the partnership between university hospitals, research centers, neurorehabilitation clinics, therapists and the industry, SwissNeuroRehab will combine the best available approaches for neuro-rehabilitation with new digital and techonological methods to create innovative and efficient therapeutic programs tailored to the individual needs of patients and their
families.

More details available here (in french).

Abstracts Accepted at Conferences on Childhood Disability

We will be presenting our work at two conferences: the Swiss Academy of Childhood Disability (SACD), 20th January 2022 and the European Academy of Childhood Disability (EACD), which will take place 18-21 May 2022 in Barcelona.

At SACD, pour paper “Serious games embedded in virtual reality as a visual rehabilitation tool for individuals with pediatric amblyopia: A protocol for a crossover randomized controlled trial“, by C. Simon-Martinez , B. Backus , B. Dornbos , M.-P. Antoniou , M. Kropp, G. Thumann, W. Bouthour, H. Steffen and P. J. Matusz, received the 2nd prize on the category of study protocols.


Award ceremony at SACD, 2nd prize on the category of study protocols

At EACD, we will present our work on “The use of DeepLabCut to detect and quantify mirror movements in children with unilateral cerebral palsy“, by B. Berclaz, H. Haberfehlner, K. Klingels, H. Feys, P. Meyns, H. Müller and C. Simon-Martinez

At EACD, Cristina Simon-Martinez is also co-author on 2 other abstracts, one with the KU Leuven and one with the University Hospital in Bern, that were accepted as oral presentations! Meet our team there to discuss all this amazing work.

Article on Hand Prostheses Control published in Frontiers in AI

Our work on “Improving Robotic Hand Prostheses Control with Eye Tracking and Computer Vision: a Multimodal Approach based on the Visuomotor Behavior of Grasping“, by M. Cognolato, M. Atzori, R. Gassert and H. Müller, was published in Frontiers in Artificial Intelligence, section Machine Learning and Artificial Intelligence.

In this paper, we investigate a multimodal approach that exploits the use of eye-hand coordination to improve the control of myoelectric hand prostheses.

Example of a typical unimodal and multimodal analysis process flow.

Full paper available open-access online: https://www.frontiersin.org/articles/10.3389/frai.2021.744476/full?&utm_source=Email_to_authors_&utm_medium=Email&utm_content=T1_11.5e1_author&utm_campaign=Email_publication&field=&journalName=Frontiers_in_Artificial_Intelligence&id=744476

Congratulations to Mara Graziani for her PhD

Mara Graziani successfully defended her PhD thesis on the interpretability of deep learning for medical image analysis. This excellent work was supervised by Prof. Stephane Marchand-Maillet (UNIGE) and, Prof. Henning Müller (HES-SO, UNIGE). Prof. Mauricio Reyes (UniBE) was part of the external committee.

Mara defending her work (left) and later literally opening the black-box model with PhD gifts (right)

FishLab : An innovative video system to observe and take a census of fish populations

FishLab, led by the COREALIS and our institute of information systems at HES-SO, will analyze near real-time fish flows and migration using machine learning algorithms on videos. Preliminary experiments with three stations located in the Rhône and the Aare rivers already enabled the improvement of the embedded system to detect and count fish.

More information (in french or in german): here

Paper on Hand Prostheses Control published in Sensors

Our paper on “Questioning Domain Adaptation in Myoelectric Hand Prostheses Control: An Inter- and Intra-Subject Study“, Giulio Marano et al. has been published in Sensors as part of a Special Issue on Biomedical Sensors for Functional Mapping.

In this study, we question the benefit of domain adaptation in transfer learning techniques (using pre-trained models obtained from prior subjects) applied to machine learning algorithms for automatic myoelectric hand prostheses control.

For more information, check the paper now available online: https://www.mdpi.com/1424-8220/21/22/7500/pdf

HECKTOR event at MICCAI 2021

On Sept. 27, we hosted the HECKTOR2021 challenge as a satellite event of MICCAI 2021. We presented the excellent results obtained by the various international teams on the tasks of tumor segmentation and outcome prediction in head and neck cancer.

We had great participation in the event with lively interaction with the participants, and a very interesting keynote by Prof. Clifton Fuller.

The Leaderboard of the challemge is available online: https://www.aicrowd.com/challenges/miccai-2021-hecktor/leaderboards

Stay tuned for the LNCS proceedings reporting the methods and results.

Thank you to all the participants, sponsors and organizers! We look forward to next year’s challenge.

Valais/Wallis AI Workshop

The 7th edition of the Valais/Wallis AI Workshop will take place on Nov. 15 2021 at the IDIAP research institute with a live youtube streaming (hybrid event).

Organized by IDIAP and HES-SO Valais, it will feature various presentations and discussions on “Energy in all its artificial states”, including a keynote presentation by Pr Guglielmina Mutani.

Full program and registration are now available online: https://www.idiap.ch/workshop/valais-wallis-ai-workshop/program

3 papers accepted at MICCAI workshops

We will be presenting our two papers on Head and Neck tumor segmentation and prognostic prediction, and one paper on colorectal cancer detection in whole slide images at MICCAI workshops Sept. 27th and Oct. 1st. Together with our paper at the main conference and the organization of the HECKTOR challenge, we are happy to have a total of five great contributions to MICCAI this year.

3D multi-modal (PET/CT) and multi-task architecture with a common down-sampling branch (green), an up-sampling segmentation branch (blue) and a radiomics branch (red).
Example of a manual annotation of primary tumor in blue and the UNet-generated automatic annotation in red on a fused PET/CT image.
Comparison between pixel-wise annotations made by a pathologist with attention maps of MuSTMIL (ours), Hashimoto et al. (2020) MSMIL and SSMIL. Cancer (red), lgd (yellow), hyperplastic poly (blue), normal tissue (orange).

Paper published in Journal of Personalized Medicine

Our review paper on harmonization methods in radiomics, in collaboration with the School for Oncology, Maastricht University, has been published in the Journal of Personalized Medicine.

The paper is now available online: “Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods“, Shruti Atul Mali et al.

Radiomics aims to extract quantitative features from medical images to improve decision support. In this work, we discuss various harmonization solutions to make the radiomic features more reproducible across various scanners and protocol settings. The different harmonization solutions are divided into two main categories: image domain (including image acquisition, post-processing of raw sensor-level image data, data augmentation, and style transfer) and feature domain (including statistical normalization, intensity harmonization, ComBat and deep learning harmonization).

Overview of harmonization methods at different stages of medical imaging.

Paper accepted in Frontiers in Computer Science

Our paper on “Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images” by Niccolò Marini et al. has been published in Frontiers in Computer Science, section Digital Public Health.

This paper describes our python library called Multi_Scale_Tools. It includes four components:

  • a pre-processing component with two methods to split the WSIs in patches from arbitrary magnification levels,
  • a CNN-based scale regressor to predict the magnification in a continuous range between 5-40x,
  • two multi-scale CNN architectures to make predictions at patch-level and
  • a multi-scale CNN architecture called HookNet to segment WSIs.

The code is available on GitHub: https://github.com/sara-nl/multi-scale-tools

An example of WSI format including multiple magnification levels.
An example of tissue represented at multiple magnification level (5x, 10x, 20x, 40x).
An example of the multicenter extraction method.
Overview of a multi-scale CNN architecture.