241 citations · 447 across the 9 of their papers we have counts for
12 papers
Geo-UNet: A Geometrically Constrained Neural Framework for Clinical-Grade Lumen Segmentation in Intravascular Ultrasound
Yiming Chen, Niharika S. D'Souza, Akshith Mandepally +9
Precisely estimating lumen boundaries in intravascular ultrasound (IVUS) is needed for sizing interventional stents to treat deep vein thrombosis (DVT). Unfortunately, current segm…
Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series
Yingcheng Liu, Neerav Karani, Neel Dey +5
The placenta plays a crucial role in fetal development. Automated 3D placenta segmentation from fetal EPI MRI holds promise for advancing prenatal care. This paper proposes an effe…
Boundary-weighted logit consistency improves calibration of segmentation networks
Neerav Karani, Neel Dey, Polina Golland
Neural network prediction probabilities and accuracy are often only weakly-correlated. Inherent label ambiguity in training data for image segmentation aggravates such miscalibrati…
A Field of Experts Prior for Adapting Neural Networks at Test Time
Neerav Karani, Georg Brunner, Ertunc Erdil +4
Performance of convolutional neural networks (CNNs) in image analysis tasks is often marred in the presence of acquisition-related distribution shifts between training and test ima…
Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation
Krishna Chaitanya, Ertunc Erdil, Neerav Karani +1
Supervised deep learning-based methods yield accurate results for medical image segmentation. However, they require large labeled datasets for this, and obtaining them is a laborio…
Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE
Anna Volokitin, Ertunc Erdil, Neerav Karani +4
Probabilistic modelling has been an essential tool in medical image analysis, especially for analyzing brain Magnetic Resonance Images (MRI). Recent deep learning techniques for es…
Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation
Krishna Chaitanya, Neerav Karani, Christian F. Baumgartner +4
Supervised learning-based segmentation methods typically require a large number of annotated training data to generalize well at test time. In medical applications, curating such d…
Contrastive learning of global and local features for medical image segmentation with limited annotations
Krishna Chaitanya, Ertunc Erdil, Neerav Karani +1
A key requirement for the success of supervised deep learning is a large labeled dataset - a condition that is difficult to meet in medical image analysis. Self-supervised learning…
Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation
Neerav Karani, Ertunc Erdil, Krishna Chaitanya +1
Convolutional Neural Networks (CNNs) work very well for supervised learning problems when the training dataset is representative of the variations expected to be encountered at tes…
Semi-Supervised and Task-Driven Data Augmentation
Krishna Chaitanya, Neerav Karani, Christian Baumgartner +3
Supervised deep learning methods for segmentation require large amounts of labelled training data, without which they are prone to overfitting, not generalizing well to unseen imag…
A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols
Neerav Karani, Krishna Chaitanya, Christian Baumgartner +1
Convolutional neural networks (CNNs) have shown promising results on several segmentation tasks in magnetic resonance (MR) images. However, the accuracy of CNNs may degrade severel…
Temporal Interpolation via Motion Field Prediction
Lin Zhang, Neerav Karani, Christine Tanner +1
Navigated 2D multi-slice dynamic Magnetic Resonance (MR) imaging enables high contrast 4D MR imaging during free breathing and provides in-vivo observations for treatment planning…