Search Results for author:
Found 53 papers, 33 papers with code
Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyond
To holistically understand cardiac health and to enable the best possible interpretation of an individual's disease risk, CMR and patient-level factors must be jointly exploited within an integrated framework.
Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling
We address this issue by proposing Energy Matching, a framework that endows flow-based approaches with the flexibility of EBMs.
vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation
While foundation models promise to alleviate this limitation, they typically fail to generalize to the task of blood vessel segmentation, posing a unique, complex problem.
FedPID: An Aggregation Method for Federated Learning
FedCostWAvg is a method that averages results by considering both the number of training samples in each group and how much the cost function decreased in the last round of training.
Whole Heart 3D+T Representation Learning Through Sparse 2D Cardiac MR Images
Cardiac Magnetic Resonance (CMR) imaging serves as the gold-standard for evaluating cardiac morphology and function.
Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes
In this work, we propose a novel interpretable-by-design method, ProtoVerse, to find relevant sub-parts of vertebral fractures (prototypes) that reliably explain the model's decision in a human-understandable way.
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge.
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge
The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets.
Topologically Faithful Multi-class Segmentation in Medical Images
Topological accuracy in medical image segmentation is a highly important property for downstream applications such as network analysis and flow modeling in vessels or cell counting.
Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers
We propose (1) a regularized edge sampling loss to effectively learn object relations in multiple domains with different numbers of edges, (2) a domain adaptation framework for image-to-graph transformers aligning image- and graph-level features from different domains, and (3) a projection function that allows using 2D data for training 3D transformers.
Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA
The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology.
Panoptica -- instance-wise evaluation of 3D semantic and instance segmentation maps
This paper introduces panoptica, a versatile and performance-optimized package designed for computing instance-wise segmentation quality metrics from 2D and 3D segmentation maps.
A skeletonization algorithm for gradient-based optimization
Finally, we demonstrate the utility of our algorithm by integrating it with two medical image processing applications that use gradient-based optimization: deep-learning-based blood vessel segmentation, and multimodal registration of the mandible in computed tomography and magnetic resonance images.
Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging
In extensive experiments using data from 40, 044 UK Biobank subjects, we demonstrate the utility and generalisability of our method for subject-specific risk prediction of CVD and the prediction of cardiac phenotypes using only ECG data.
Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image Modeling
We evaluate our approach on 92 in-house 2D+t cardiac MR subjects and compare it to MR reconstruction methods with image-domain regularizers.
GRAtt-VIS: Gated Residual Attention for Auto Rectifying Video Instance Segmentation
Secondly, we propose a novel inter-instance interaction using gate activation as a mask for self-attention.
Ranked #6 on
Video Instance Segmentation
on YouTube-VIS 2021
(using extra training data)
The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting
The challenge is organized as part of the ASNR-BraTS MICCAI challenge.
FedPIDAvg: A PID controller inspired aggregation method for Federated Learning
Another key challenge was the vastly varying size of data samples per center.
Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations
Clinical routine and retrospective cohorts commonly include multi-parametric Magnetic Resonance Imaging; however, they are mostly acquired in different anisotropic 2D views due to signal-to-noise-ratio and scan-time constraints.
Identifying chromophore fingerprints of brain tumor tissue on hyperspectral imaging using principal component analysis
In this article, we perform a statistical analysis of the brain tumor patients' HSI scans from the HELICoiD dataset with the aim of identifying the correlation between reflectance spectra and absorption spectra of tissue chromophores.
A Domain-specific Perceptual Metric via Contrastive Self-supervised Representation: Applications on Natural and Medical Images
We find that in the natural image domain, CSR behaves on par with the supervised one on several perceptual tests as a metric, and in the medical domain, CSR better quantifies perceptual similarity concerning the experts' ratings.
Topologically faithful image segmentation via induced matching of persistence barcodes
In this work, we propose the first topologically and feature-wise accurate metric and loss function for supervised image segmentation, which we term Betti matching.
InstanceFormer: An Online Video Instance Segmentation Framework
We propose three novel components to model short-term and long-term dependency and temporal coherence.
Ranked #6 on
Video Instance Segmentation
on Youtube-VIS 2022 Validation
(using extra training data)
Focused Decoding Enables 3D Anatomical Detection by Transformers
To tackle this issue and facilitate the advent of medical Detection Transformers, we propose a novel Detection Transformer for 3D anatomical structure detection, dubbed Focused Decoder.
Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings
To approximate human quality ratings on scarce expert data, we train surrogate quality estimation models.
blob loss: instance imbalance aware loss functions for semantic segmentation
\emph{Blob loss} is designed for semantic segmentation problems where detecting multiple instances matters.
Relationformer: A Unified Framework for Image-to-Graph Generation
We leverage direct set-based object prediction and incorporate the interaction among the objects to learn an object-relation representation jointly.
A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning
Automatic localization and segmentation of organs-at-risk (OAR) in CT are essential pre-processing steps in medical image analysis tasks, such as radiation therapy planning.
Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challenge
Importantly, we explore the inclusion of a transformer in the bottleneck of the U-Net architecture.
FedCostWAvg: A new averaging for better Federated Learning
We propose a simple new aggregation strategy for federated learning that won the MICCAI Federated Tumor Segmentation Challenge 2021 (FETS), the first ever challenge on Federated Learning in the Machine Learning community.
Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling
Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration.
Partial supervision for the FeTA challenge 2021
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.
Semi-Implicit Neural Solver for Time-dependent Partial Differential Equations
In this paper, we propose a neural solver to learn an optimal iterative scheme in a data-driven fashion for any class of PDEs.
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)
Moreover, we benchmark numerous state-of-the-art graph learning algorithms on the biologically relevant tasks of vessel prediction and vessel classification using the introduced vessel graph dataset.
clDice - A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research.
Evaluating the Robustness of Self-Supervised Learning in Medical Imaging
Self-supervision has demonstrated to be an effective learning strategy when training target tasks on small annotated data-sets.
METGAN: Generative Tumour Inpainting and Modality Synthesis in Light Sheet Microscopy
Novel multimodal imaging methods are capable of generating extensive, super high resolution datasets for preclinical research.
Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient
It is often unclear how to optimize abstract metrics, such as human expert perception, in convolutional neural network (CNN) training.
The MICCAI Hackathon on reproducibility, diversity, and selection of papers at the MICCAI conference
With this growth, however, come new challenges for the community.
An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset
It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders.
Cranial Implant Prediction using Low-Resolution 3D Shape Completion and High-Resolution 2D Refinement
The first subnetwork is designed to complete the shape of the downsampled defective skull.
Automated Claustrum Segmentation in Human Brain MRI Using Deep Learning
In the last two decades, neuroscience has produced intriguing evidence for a central role of the claustrum in mammalian forebrain structure and function.
A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images
Raster-scan optoacoustic mesoscopy (RSOM) is a powerful, non-invasive optical imaging technique for functional, anatomical, and molecular skin and tissue analysis.
Red-GAN: Attacking class imbalance via conditioned generation. Yet another perspective on medical image synthesis for skin lesion dermoscopy and brain tumor MRI
Exploiting learning algorithms under scarce data regimes is a limitation and a reality of the medical imaging field.
Relation Transformer Network
In this work, we propose a novel transformer formulation for scene graph generation and relation prediction.
clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research.
Red-GAN: Attacking class imbalance via conditioned generation. Yet another medical imaging perspective
Exploiting learning algorithms under scarce data regimes is a limitation and a reality of the medical imaging field.
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf. io/nqjyw/, https://osf. io/t98fz/).
Implicit Neural Solver for Time-dependent Linear PDEs with Convergence Guarantee
The existing implicit schemes are usually iterative and employ a general-purpose solver which may be sub-optimal for a specific class of PDEs.
Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation
Multi-organ segmentation in whole-body computed tomography (CT) is a constant pre-processing step which finds its application in organ-specific image retrieval, radiotherapy planning, and interventional image analysis.
Neural parameters estimation for brain tumor growth modeling
In this work, we propose a learning-based technique for the estimation of tumor growth model parameters from medical scans.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
