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An unsupervised learning-based shear wave tracking method for ultrasound elastography
Remi Delaunay, Yipeng Hu, Tom Vercauteren
Shear wave elastography involves applying a non-invasive acoustic radiation force to the tissue and imaging the induced deformation to infer its mechanical properties. This work in…
DEEPBEAS3D: Deep Learning and B-Spline Explicit Active Surfaces
Helena Williams, João Pedrosa, Muhammad Asad +4
Deep learning-based automatic segmentation methods have become state-of-the-art. However, they are often not robust enough for direct clinical application, as domain shifts between…
Deep Homography Prediction for Endoscopic Camera Motion Imitation Learning
Martin Huber, Sebastien Ourselin, Christos Bergeles +1
In this work, we investigate laparoscopic camera motion automation through imitation learning from retrospective videos of laparoscopic interventions. A novel method is introduced…
Synthetic white balancing for intra-operative hyperspectral imaging
Anisha Bahl, Conor C. Horgan, Mirek Janatka +10
Hyperspectral imaging shows promise for surgical applications to non-invasively provide spatially-resolved, spectral information. For calibration purposes, a white reference image…
Learning-based sound speed estimation and aberration correction in linear-array photoacoustic imaging
Mengjie Shi, Tom Vercauteren, Wenfeng Xia
Photoacoustic (PA) image reconstruction involves acoustic inversion that necessitates the specification of the speed of sound (SoS) within the medium of propagation. Due to the lac…
DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images
Andres Diaz-Pinto, Pritesh Mehta, Sachidanand Alle +17
Automatic segmentation of medical images is a key step for diagnostic and interventional tasks. However, achieving this requires large amounts of annotated volumes, which can be te…
Hyperspectral Image Segmentation: A Preliminary Study on the Oral and Dental Spectral Image Database (ODSI-DB)
Luis C. Garcia-Peraza-Herrera, Conor Horgan, Sebastien Ourselin +2
Visual discrimination of clinical tissue types remains challenging, with traditional RGB imaging providing limited contrast for such tasks. Hyperspectral imaging (HSI) is a promisi…
Adaptive Multi-scale Online Likelihood Network for AI-assisted Interactive Segmentation
Muhammad Asad, Helena Williams, Indrajeet Mandal +4
Existing interactive segmentation methods leverage automatic segmentation and user interactions for label refinement, significantly reducing the annotation workload compared to man…
VideoSum: A Python Library for Surgical Video Summarization
Luis C. Garcia-Peraza-Herrera, Sebastien Ourselin, Tom Vercauteren
The performance of deep learning (DL) algorithms is heavily influenced by the quantity and the quality of the annotated data. However, in Surgical Data Science, access to it is lim…
Spatial gradient consistency for unsupervised learning of hyperspectral demosaicking: Application to surgical imaging
Peichao Li, Muhammad Asad, Conor Horgan +3
Hyperspectral imaging has the potential to improve intraoperative decision making if tissue characterisation is performed in real-time and with high-resolution. Hyperspectral snaps…
Trackerless freehand ultrasound with sequence modelling and auxiliary transformation over past and future frames
Qi Li, Ziyi Shen, Qian Li +5
Three-dimensional (3D) freehand ultrasound (US) reconstruction without a tracker can be advantageous over its two-dimensional or tracked counterparts in many clinical applications.…
Spatiotemporal singular value decomposition for denoising in photoacoustic imaging with low-energy excitation light source
Mengjie Shi, Tom Vercauteren, Wenfeng Xia
Photoacoustic (PA) imaging is an emerging hybrid imaging modality that combines rich optical spectroscopic contrast and high ultrasonic resolution and thus holds tremendous promise…
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Kelly Payette, Hongwei Li, Priscille de Dumast +55
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital ste…
A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation
Lucas Fidon, Michael Aertsen, Florian Kofler +18
Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, wi…
ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation
Muhammad Asad, Lucas Fidon, Tom Vercauteren
Automatic segmentation of lung lesions associated with COVID-19 in CT images requires large amount of annotated volumes. Annotations mandate expert knowledge and are time-intensive…
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
Reuben Dorent, Aaron Kujawa, Marina Ivory +37
Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. While a large variety of DA techniques has been proposed for image segmentation, most…
Partial supervision for the FeTA challenge 2021
Lucas Fidon, Michael Aertsen, Suprosanna Shit +4
This paper describes our method for our participation in the FeTA challenge2021 (team name: TRABIT). The performance of convolutional neural networks for medical image segmentation…
Deep Homography Estimation in Dynamic Surgical Scenes for Laparoscopic Camera Motion Extraction
Martin Huber, Sébastien Ourselin, Christos Bergeles +1
Current laparoscopic camera motion automation relies on rule-based approaches or only focuses on surgical tools. Imitation Learning (IL) methods could alleviate these shortcomings,…
Homography-based Visual Servoing with Remote Center of Motion for Semi-autonomous Robotic Endoscope Manipulation
Martin Huber, John Bason Mitchell, Ross Henry +3
The dominant visual servoing approaches in Minimally Invasive Surgery (MIS) follow single points or adapt the endoscope's field of view based on the surgical tools' distance. These…
Interactive Segmentation via Deep Learning and B-Spline Explicit Active Surfaces
Helena Williams, João Pedrosa, Laura Cattani +4
Automatic medical image segmentation via convolutional neural networks (CNNs) has shown promising results. However, they may not always be robust enough for clinical use. Sub-optim…
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction
Peichao Li, Michael Ebner, Philip Noonan +5
Hyperspectral imaging is one of the most promising techniques for intraoperative tissue characterisation. Snapshot mosaic cameras, which can capture hyperspectral data in a single…
Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation
Lucas Fidon, Michael Aertsen, Doaa Emam +9
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. Methods…
Intrapapillary Capillary Loop Classification in Magnification Endoscopy: Open Dataset and Baseline Methodology
Luis C. Garcia-Peraza-Herrera, Martin Everson, Laurence Lovat +6
Purpose. Early squamous cell neoplasia (ESCN) in the oesophagus is a highly treatable condition. Lesions confined to the mucosal layer can be curatively treated endoscopically. We…
Scale factor point spread function matching: Beyond aliasing in image resampling
M. Jorge Cardoso, Marc Modat, Tom Vercauteren +1
Imaging devices exploit the Nyquist-Shannon sampling theorem to avoid both aliasing and redundant oversampling by design. Conversely, in medical image resampling, images are consid…