Search Results for author:
Found 31 papers, 13 papers with code
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.
Framing image registration as a landmark detection problem for better representation of clinical relevance
Nowadays, registration methods are typically evaluated based on sub-resolution tracking error differences.
The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI
Clinical monitoring of metastatic disease to the brain can be a laborious and time-consuming process, especially in cases involving multiple metastases when the assessment is performed manually.
The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)
Thus, the BraTS-Africa Challenge provides a unique opportunity to include brain MRI glioma cases from SSA in global efforts through the BraTS Challenge to develop and evaluate computer-aided-diagnostic (CAD) methods for the detection and characterization of glioma in resource-limited settings, where the potential for CAD tools to transform healthcare are more likely.
The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children.
The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting
The challenge is organized as part of the BraTS 2023 challenge hosted at the MICCAI 2023 conference in Vancouver, Canada.
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)
In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023.
The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma
Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality.
Primitive Simultaneous Optimization of Similarity Metrics for Image Registration
Even though simultaneous optimization of similarity metrics is a standard procedure in the field of semantic segmentation, surprisingly, this is much less established for image registration.
Why is the winner the best?
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
Understanding metric-related pitfalls in image analysis validation
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.
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.
Approaching Peak Ground Truth
Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such.
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels.
ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
The test dataset will be used for model validation only and will not be released to the public.
Metrics reloaded: Recommendations for image analysis validation
The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output.
blob loss: instance imbalance aware loss functions for semantic segmentation
\emph{Blob loss} is designed for semantic segmentation problems where detecting multiple instances matters.
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.
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation
Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels.
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.
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.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
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.
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.
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.
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.
DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis
Synthesizing MR imaging sequences is highly relevant in clinical practice, as single sequences are often missing or are of poor quality (e. g. due to motion).
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.
