Publications associées à Segmentation of head and neck lymph node regions for radiotherapy planning, using active contour based atlas registration

Computation of sensitivity coefficients in fixed source simulations with SERPENT2

Within the scope of the implementation of a nuclear data pipeline aiming at producing the best possible evaluated nuclear data files, a major point is the production of relevant sensitivity coefficients when including integral benchmark information. Thanks ...

Elsevier Science Sa2024

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

Pascal Fua, Sean Lewis Hill, Jiancheng Yang, Xiang Li, Amos Sironi, Jian Zhou, Jie Zhou, Siqi Liu

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in m ...

2023

Compressed sensing (CS) MP2RAGE versus standard MPRAGE: A of derived brain volume measurements

Tobias Kober, Tom Hilbert, Gian Franco Piredda

Purpose: T1 Magnetization Prepared Two Rapid Acquisition Gradient Echo (MP2RAGE) with compress sensing (CS) has been proposed as an improvement of the standard MPRAGE sequence with multiple advantages including reduced acquisition time needed to provide a ...

ELSEVIER SCI LTD2022

Weakly Supervised Volumetric Image Segmentation with Deformed Templates

Pascal Fua, Pamuditha Udaranga Wickramasinghe, Patrick Moller Jensen, Mian Akbar Shah

There are many approaches to weakly-supervised training of networks to segment 2D images. By contrast, existing approaches to segmenting volumetric images rely on full-supervision of a subset of 2D slices of the 3D volume. We propose an approach to volume ...

Springer2022

Evaluating salient object detection in natural images with multiple objects having multi-level saliency

Sabine Süsstrunk, Gökhan Yildirim

Salient object detection is evaluated using binary ground truth (GT) with the labels being salient object class and background. In this study, the authors corroborate based on three subjective experiments on a novel image dataset that objects in natural im ...

2020

Myeloid Cells Orchestrate Systemic Immunosuppression, Impairing the Efficacy of Immunotherapy against HPV+ Cancers

Douglas Hanahan, Nadine Fournier, Mélanie Louise Tichet, Melody Swartz, Stephan Wullschleger, Gabriele Galliverti

Cancers induced by human papillomaviruses (HPV) should be responsive to immunotherapy by virtue of expressing the immunogenic oncoproteins E6/E7. However, advanced forms of cervical cancer, driven by HPV, are poorly responsive to immune response-enhancing ...

AMER ASSOC CANCER RESEARCH2020

Model-Class Selection Using Clustering and Classification for Structural Identification and Prediction

Ian Smith, Sai Ganesh Sarvotham Pai, Masoud Sanayei

Structural identification using physics-based models and subsequent prediction have much potential to enhance civil infrastructure asset-management decision-making. Interpreting monitoring information in the presence of multiple uncertainty sources and sys ...

2021

Comparison of crack segmentation using digital image correlation measurements and deep learning

Katrin Beyer, Radhakrishna Achanta, Amir Rezaie, Michele Godio

Reliable methods for detecting pixels that represent cracks from laboratory images taken for digital image correlation (DIC) are required for two main reasons. Firstly, the segmented crack maps are used as an input for some DIC methods that are based on di ...

2020

Automated CT bone segmentation using statistical shape modelling and local template matching

Alexandre Terrier, Alain Farron, Elham Taghizadeh, Philippe Büchler

Accurate CT bone segmentation is essential to develop chair-side manufacturing of implants based on additive manufacturing. We herewith present an automated method able to accurately segment challenging bone regions, while simultaneously providing anatomic ...

2019

Geometry in active learning for binary and multi-class image segmentation

Pascal Fua, Raphael Sznitman, Ksenia Konyushkova

We propose an active learning approach to image segmentation that exploits geometric priors to speed up and streamline the annotation process. It can be applied for both background foreground and multi-class segmentation tasks in 2D images and 3D image vol ...

2019