31 citations · 49 across the 17 of their papers we have counts for
8 papers · 2 filters
Deep Learning with Mixed Supervision for Brain Tumor Segmentation
Pawel Mlynarski, Hervé Delingette, Antonio Criminisi +1
Most of the current state-of-the-art methods for tumor segmentation are based on machine learning models trained on manually segmented images. This type of training data is particu…
Automatically Segmenting the Left Atrium from Cardiac Images Using Successive 3D U-Nets and a Contour Loss
Shuman Jia, Antoine Despinasse, Zihao Wang +5
Radiological imaging offers effective measurement of anatomy, which is useful in disease diagnosis and assessment. Previous study has shown that the left atrial wall remodeling can…
Learning a Probabilistic Model for Diffeomorphic Registration
Julian Krebs, Hervé Delingette, Boris Mailhé +2
We propose to learn a low-dimensional probabilistic deformation model from data which can be used for registration and the analysis of deformations. The latent variable model maps…
Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow
Qiao Zheng, Hervé Delingette, Nicholas Ayache
We propose a method to classify cardiac pathology based on a novel approach to extract image derived features to characterize the shape and motion of the heart. An original semi-su…
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context
Pawel Mlynarski, Hervé Delingette, Antonio Criminisi +1
We present an efficient deep learning approach for the challenging task of tumor segmentation in multisequence MR images. In recent years, Convolutional Neural Networks (CNN) have…
3D Consistent & Robust Segmentation of Cardiac Images by Deep Learning with Spatial Propagation
Qiao Zheng, Hervé Delingette, Nicolas Duchateau +1
We propose a method based on deep learning to perform cardiac segmentation on short axis MRI image stacks iteratively from the top slice (around the base) to the bottom slice (arou…