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20162023
most citedA Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology

271 citations · 603 across the 47 of their papers we have counts for

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Showing 2019Show all

9 papers · 1 filter

eess.IV2019

Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation

Ilkay Oksuz, James R. Clough, Bram Ruijsink +6

Segmenting anatomical structures in medical images has been successfully addressed with deep learning methods for a range of applications. However, this success is heavily dependen…

cs.CV2019★ 271 cited

A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology

James R. Clough, Nicholas Byrne, Ilkay Oksuz +3

We introduce a method for training neural networks to perform image or volume segmentation in which prior knowledge about the topology of the segmented object can be explicitly pro…

cs.CV2019

Model-Based and Data-Driven Strategies in Medical Image Computing

Daniel Rueckert, Julia A. Schnabel

Model-based approaches for image reconstruction, analysis and interpretation have made significant progress over the last decades. Many of these approaches are based on either math…

cs.LG2019★ 22 cited

dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance

Jo Schlemper, Ilkay Oksuz, James R. Clough +5

AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited. We present dAUTOMAP, a novel way for decomposing the…

eess.IV2019

Global and Local Interpretability for Cardiac MRI Classification

James R. Clough, Ilkay Oksuz, Esther Puyol-Anton +3

Deep learning methods for classifying medical images have demonstrated impressive accuracy in a wide range of tasks but often these models are hard to interpret, limiting their app…

eess.IV2019★ 1 cited

Mechanically Powered Motion Imaging Phantoms: Proof of Concept

Alberto Gomez, Cornelia Schmitz, Markus Henningsson +8

Motion imaging phantoms are expensive, bulky and difficult to transport and set-up. The purpose of this paper is to demonstrate a simple approach to the design of multi-modality mo…

cs.CV2019★ 1 cited

FastReg: Fast Non-Rigid Registration via Accelerated Optimisation on the Manifold of Diffeomorphisms

Daniel Grzech, Loïc le Folgoc, Mattias P. Heinrich +5

We present an implementation of a new approach to diffeomorphic non-rigid registration of medical images. The method is based on optical flow and warps images via gradient flow wit…

eess.IV2019★ 74 cited

Evaluation of MRI to ultrasound registration methods for brain shift correction: The CuRIOUS2018 Challenge

Yiming Xiao, Hassan Rivaz, Matthieu Chabanas +13

In brain tumor surgery, the quality and safety of the procedure can be impacted by intra-operative tissue deformation, called brain shift. Brain shift can move the surgical targets…

cs.CV2019★ 1 cited

Explicit topological priors for deep-learning based image segmentation using persistent homology

James R. Clough, Ilkay Oksuz, Nicholas Byrne +2

We present a novel method to explicitly incorporate topological prior knowledge into deep learning based segmentation, which is, to our knowledge, the first work to do so. Our meth…