2.7k citations · 9.2k across the 86 of their papers we have counts for
43 papers · 1 filter
UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation
Jianghao Wu, Guotai Wang, Ran Gu +6
Domain Adaptation (DA) is important for deep learning-based medical image segmentation models to deal with testing images from a new target domain. As the source-domain data are us…
Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations
Reuben Dorent, Nazim Haouchine, Fryderyk Kögl +9
We introduce MHVAE, a deep hierarchical variational auto-encoder (VAE) that synthesizes missing images from various modalities. Extending multi-modal VAEs with a hierarchical laten…
Privileged Anatomical and Protocol Discrimination in Trackerless 3D Ultrasound Reconstruction
Qi Li, Ziyi Shen, Qian Li +5
Three-dimensional (3D) freehand ultrasound (US) reconstruction without using any additional external tracking device has seen recent advances with deep neural networks (DNNs). In t…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
SegMatch: A semi-supervised learning method for surgical instrument segmentation
Meng Wei, Charlie Budd, Luis C. Garcia-Peraza-Herrera +3
Surgical instrument segmentation is recognised as a key enabler in providing advanced surgical assistance and improving computer-assisted interventions. In this work, we propose Se…
Deep Reinforcement Learning Based System for Intraoperative Hyperspectral Video Autofocusing
Charlie Budd, Jianrong Qiu, Oscar MacCormac +7
Hyperspectral imaging (HSI) captures a greater level of spectral detail than traditional optical imaging, making it a potentially valuable intraoperative tool when precise tissue d…
LoViT: Long Video Transformer for Surgical Phase Recognition
Yang Liu, Maxence Boels, Luis C. Garcia-Peraza-Herrera +4
Online surgical phase recognition plays a significant role towards building contextual tools that could quantify performance and oversee the execution of surgical workflows. Curren…
Text Promptable Surgical Instrument Segmentation with Vision-Language Models
Zijian Zhou, Oluwatosin Alabi, Meng Wei +2
In this paper, we propose a novel text promptable surgical instrument segmentation approach to overcome challenges associated with diversity and differentiation of surgical instrum…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Rapid and robust endoscopic content area estimation: A lean GPU-based pipeline and curated benchmark dataset
Charlie Budd, Luis C. Garcia-Peraza-Herrera, Martin Huber +2
Endoscopic content area refers to the informative area enclosed by the dark, non-informative, border regions present in most endoscopic footage. The estimation of the content area…
Can segmentation models be trained with fully synthetically generated data?
Virginia Fernandez, Walter Hugo Lopez Pinaya, Pedro Borges +4
In order to achieve good performance and generalisability, medical image segmentation models should be trained on sizeable datasets with sufficient variability. Due to ethics and g…
Driving Points Prediction For Abdominal Probabilistic Registration
Samuel Joutard, Reuben Dorent, Sebastien Ourselin +2
Inter-patient abdominal registration has various applications, from pharmakinematic studies to anatomy modeling. Yet, it remains a challenging application due to the morphological…
FastGeodis: Fast Generalised Geodesic Distance Transform
Muhammad Asad, Reuben Dorent, Tom Vercauteren
The FastGeodis package provides an efficient implementation for computing Geodesic and Euclidean distance transforms (or a mixture of both), targeting efficient utilisation of CPU…
Cross-Modality Image Registration using a Training-Time Privileged Third Modality
Qianye Yang, David Atkinson, Yunguan Fu +7
In this work, we consider the task of pairwise cross-modality image registration, which may benefit from exploiting additional images available only at training time from an additi…
Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings
Florian Kofler, Ivan Ezhov, Lucas Fidon +14
Human ratings are abstract representations of segmentation quality. To approximate human quality ratings on scarce expert data, we train surrogate quality estimation models. We eva…
blob loss: instance imbalance aware loss functions for semantic segmentation
Florian Kofler, Suprosanna Shit, Ivan Ezhov +14
Deep convolutional neural networks (CNN) have proven to be remarkably effective in semantic segmentation tasks. Most popular loss functions were introduced targeting improved volum…
Motion Correction and Volumetric Reconstruction for Fetal Functional Magnetic Resonance Imaging Data
Daniel Sobotka, Michael Ebner, Ernst Schwartz +8
Motion correction is an essential preprocessing step in functional Magnetic Resonance Imaging (fMRI) of the fetal brain with the aim to remove artifacts caused by fetal movement an…
Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI
Lucas Fidon, Michael Aertsen, Nada Mufti +13
The performance of deep neural networks typically increases with the number of training images. However, not all images have the same importance towards improved performance and ro…
Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation
Reuben Dorent, Samuel Joutard, Jonathan Shapey +4
We introduce , a weakly supervised 3D approach to train a deep image segmentation network using particularly weak train-time annotations: only 6 extreme clicks…
MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning
Xiangde Luo, Guotai Wang, Tao Song +6
Segmentation of organs or lesions from medical images plays an essential role in many clinical applications such as diagnosis and treatment planning. Though Convolutional Neural Ne…
Real-Time Segmentation of Non-Rigid Surgical Tools based on Deep Learning and Tracking
Luis C. García-Peraza-Herrera, Wenqi Li, Caspar Gruijthuijsen +7
Real-time tool segmentation is an essential component in computer-assisted surgical systems. We propose a novel real-time automatic method based on Fully Convolutional Networks (FC…
Permutohedral Attention Module for Efficient Non-Local Neural Networks
Samuel Joutard, Reuben Dorent, Amanda Isaac +3
Medical image processing tasks such as segmentation often require capturing non-local information. As organs, bones, and tissues share common characteristics such as intensity, sha…
Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy
Daniele Ravì, Agnieszka Barbara Szczotka, Stephen P Pereira +1
In recent years, endomicroscopy has become increasingly used for diagnostic purposes and interventional guidance. It can provide intraoperative aids for real-time tissue characteri…
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation
Guotai Wang, Wenqi Li, Sebastien Ourselin +1
Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) have…