Search Results for author: Jan Deprest

Found 21 papers, 13 papers with code

Partial supervision for the FeTA challenge 2021

2 code implementations3 Nov 2021 Lucas Fidon, Michael Aertsen, Suprosanna Shit, Philippe Demaerel, Sébastien Ourselin, Jan Deprest, Tom Vercauteren

Label-set loss functions allow to train deep neural networks with partially segmented images, i. e. segmentations in which some classes may be grouped into super-classes.

Brain Segmentation Semantic Segmentation

Interactive Segmentation via Deep Learning and B-Spline Explicit Active Surfaces

no code implementations25 Oct 2021 Helena Williams, João Pedrosa, Laura Cattani, Susanne Housmans, Tom Vercauteren, Jan Deprest, Jan D'hooge

The interactive element of the framework allows the user to precisely edit the contour in real-time, and by utilising BEAS it ensures the final contour is smooth and anatomically plausible.

Interactive Segmentation Medical Image Segmentation +1

Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation

2 code implementations8 Jul 2021 Lucas Fidon, Michael Aertsen, Doaa Emam, Nada Mufti, Frédéric Guffens, Thomas Deprest, Philippe Demaerel, Anna L. David, Andrew Melbourne, Sébastien Ourselin, Jan Deprest, Tom Vercauteren

Deep neural networks have increased the accuracy of automatic segmentation, however, their accuracy depends on the availability of a large number of fully segmented images.

MRI segmentation

FetReg: Placental Vessel Segmentation and Registration in Fetoscopy Challenge Dataset

1 code implementation10 Jun 2021 Sophia Bano, Alessandro Casella, Francisco Vasconcelos, Sara Moccia, George Attilakos, Ruwan Wimalasundera, Anna L. David, Dario Paladini, Jan Deprest, Elena De Momi, Leonardo S. Mattos, Danail Stoyanov

Through the \textit{Fetoscopic Placental Vessel Segmentation and Registration (FetReg)} challenge, we present a large-scale multi-centre dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms for the fetal environment with a focus on creating drift-free mosaics from long duration fetoscopy videos.

Semantic Segmentation

MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning

1 code implementation25 Apr 2021 Xiangde Luo, Guotai Wang, Tao Song, Jingyang Zhang, Michael Aertsen, Jan Deprest, Sebastien Ourselin, Tom Vercauteren, Shaoting Zhang

To solve these problems, we propose a novel deep learning-based interactive segmentation method that not only has high efficiency due to only requiring clicks as user inputs but also generalizes well to a range of previously unseen objects.

Interactive Segmentation Medical Image Segmentation +1

Active Annotation of Informative Overlapping Frames in Video Mosaicking Applications

1 code implementation30 Dec 2020 Loic Peter, Marcel Tella-Amo, Dzhoshkun Ismail Shakir, Jan Deprest, Sebastien Ourselin, Juan Eugenio Iglesias, Tom Vercauteren

In addition to the efficient construction of a mosaic, our framework provides, as a by-product, ground truth landmark correspondences which can be used for evaluation or learning purposes.

Frame

CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation

1 code implementation22 Sep 2020 Ran Gu, Guotai Wang, Tao Song, Rui Huang, Michael Aertsen, Jan Deprest, Sébastien Ourselin, Tom Vercauteren, Shaoting Zhang

Also, we propose a scale attention module implicitly emphasizing the most salient feature maps among multiple scales so that the CNN is adaptive to the size of an object.

Lesion Segmentation Semantic Segmentation +1

Deep Placental Vessel Segmentation for Fetoscopic Mosaicking

1 code implementation8 Jul 2020 Sophia Bano, Francisco Vasconcelos, Luke M. Shepherd, Emmanuel Vander Poorten, Tom Vercauteren, Sebastien Ourselin, Anna L. David, Jan Deprest, Danail Stoyanov

We propose a solution utilising the U-Net architecture for performing placental vessel segmentation in fetoscopic videos.

Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices

1 code implementation2 Jul 2020 Guotai Wang, Michael Aertsen, Jan Deprest, Sebastien Ourselin, Tom Vercauteren, Shaoting Zhang

Experimental results show that: (1) our proposed CNN obtains uncertainty estimation in real time which correlates well with mis-segmentations, (2) the proposed interactive level set is effective and efficient for refinement, (3) UGIR obtains accurate refinement results with around 30% improvement of efficiency by using uncertainty to guide user interactions.

Brain Segmentation

Deep Sequential Mosaicking of Fetoscopic Videos

1 code implementation15 Jul 2019 Sophia Bano, Francisco Vasconcelos, Marcel Tella Amo, George Dwyer, Caspar Gruijthuijsen, Jan Deprest, Sebastien Ourselin, Emmanuel Vander Poorten, Tom Vercauteren, Danail Stoyanov

Mosaicking can align multiple overlapping images to generate an image with increased FoV, however, existing techniques apply poorly to fetoscopy due to the low visual quality, texture paucity, and hence fail in longer sequences due to the drift accumulated over time.

Data Augmentation

Retrieval and Registration of Long-Range Overlapping Frames for Scalable Mosaicking of In Vivo Fetoscopy

no code implementations28 Feb 2018 Loïc Peter, Marcel Tella-Amo, Dzhoshkun Ismail Shakir, George Attilakos, Ruwan Wimalasundera, Jan Deprest, Sébastien Ourselin, Tom Vercauteren

Robustness to visual challenges during registration and long-range temporal consistency are proposed, offering first positive results on in vivo data for which standard mosaicking techniques are not applicable.

A Log-Euclidean and Total Variation based Variational Framework for Computational Sonography

no code implementations6 Feb 2018 Jyotirmoy Banerjee, Premal A. Patel, Fred Ushakov, Donald Peebles, Jan Deprest, Sebastien Ourselin, David Hawkes, Tom Vercauteren

We propose a spatial compounding technique and variational framework to improve 3D ultrasound image quality by compositing multiple ultrasound volumes acquired from different probe orientations.

Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network

no code implementations18 Dec 2017 Ester Bonmati, Yipeng Hu, Nikhil Sindhwani, Hans Peter Dietz, Jan D'hooge, Dean Barratt, Jan Deprest, Tom Vercauteren

Results show a median Dice similarity coefficient of 0. 90 with an interquartile range of 0. 08, with equivalent performance to the three operators (with a Williams' index of 1. 03), and outperforming a U-Net architecture without the need for batch normalisation.

Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning

no code implementations11 Oct 2017 Guotai Wang, Wenqi Li, Maria A. Zuluaga, Rosalind Pratt, Premal A. Patel, Michael Aertsen, Tom Doel, Anna L. David, Jan Deprest, Sebastien Ourselin, Tom Vercauteren

Experimental results show that 1) our model is more robust to segment previously unseen objects than state-of-the-art CNNs; 2) image-specific fine-tuning with the proposed weighted loss function significantly improves segmentation accuracy; and 3) our method leads to accurate results with fewer user interactions and less user time than traditional interactive segmentation methods.

Interactive Segmentation Medical Image Segmentation +1

DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

1 code implementation3 Jul 2017 Guotai Wang, Maria A. Zuluaga, Wenqi Li, Rosalind Pratt, Premal A. Patel, Michael Aertsen, Tom Doel, Anna L. David, Jan Deprest, Sebastien Ourselin, Tom Vercauteren

We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy.

Brain Tumor Segmentation Interactive Segmentation +2

ToolNet: Holistically-Nested Real-Time Segmentation of Robotic Surgical Tools

no code implementations25 Jun 2017 Luis C. Garcia-Peraza-Herrera, Wenqi Li, Lucas Fidon, Caspar Gruijthuijsen, Alain Devreker, George Attilakos, Jan Deprest, Emmanuel Vander Poorten, Danail Stoyanov, Tom Vercauteren, Sebastien Ourselin

We propose the use of parametric rectified linear units for semantic labeling in these small architectures to increase the regularization ability of the design and maintain the segmentation accuracy without overfitting the training sets.

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