In parallel to my PhD work, I have developed open, standalone software for C++ and Python, mainlyusing Qt5/PySide6 (e.g.,FastPathology andRaidionics). I have published open command line tools(e.g.,livermask), developed Python packages (e.g.,gradient-accumulator,torchstain),and published articles to high-impact scientific journals related to medical image analysis anddeep learning (on topics such asimage classification,semantic segmentation,image-to-image registration,high-performance computing,semi-supervised learning,andnatural language processing).

I have also written abook chapterand acted as a reviewer for scientific journals, such asMedical Image Analysis,Nature Scientific Reports,Frontiers in Medicine,IJCARS,QIMS, andBMC Medical Imaging.Lastly, I have (co-)supervised five Master’s students working on using deep learning forsupervised/semi-supervised segmentation of 3D medical images (CT), multilabelhistopathology image classification, and bronchoscopy video navigation.

news

today's publication

1. Comp.M.Bio

   Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding

   David Bouget, André Pedersen, Johanna Vanel, Haakon O. Leira, and Thomas Langø

   Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Mar 2022

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   As lung cancer evolves, the presence of potentially malignant lymph nodes must be assessed to properly estimate disease progression and select the best treatment strategy. A method for accurate and automatic segmentation is hence decisive for quantitatively describing lymph nodes. In this study, the use of 3D convolutional neural networks, either through slab-wise schemes or the leveraging of downsampled entire volumes, is investigated. As lymph nodes have similar attenuation values to nearby anatomical structures, we use the knowledge of other organs as prior information to guide the segmentation. To assess the performances, a 5-fold cross-validation strategy was followed over a dataset of 120 contrast-enhanced CT volumes. For the 1178 lymph nodes with a short-axis diameter ≥10 mm, our best-performing approach reached a patient-wise recall of 92%, a false positive per patient ratio of 5 and a segmentation overlap of 80.5%. Fusing a slab-wise and a full volume approach within an ensemble scheme generated the best performances. The anatomical priors guiding strategy is promising, yet a larger set than four organs appears needed to generate an optimal benefit. A larger dataset is also mandatory given the wide range of expressions a lymph node can exhibit (i.e. shape, location and attenuation).

   @article{bouget2021mediastinal,abbr={Comp.M.Bio},bibtex_show={true},author={Bouget, David and Pedersen, André and Vanel, Johanna and Leira, Haakon O. and Langø, Thomas},title={Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding},journal={Computer Methods in Biomechanics and Biomedical Engineering: Imaging \& Visualization},volume={0},number={0},pages={1-15},year={2022},month=mar,pdf={https://www.tandfonline.com/doi/full/10.1080/21681163.2022.2043778},code={https://github.com/dbouget/ct_mediastinal_structures_segmentation},publisher={Taylor & Francis},doi={10.1080/21681163.2022.2043778},url={https://doi.org/10.1080/21681163.2022.2043778},eprint={https://doi.org/10.1080/21681163.2022.2043778},rgvalue={12}}