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Found 62 papers, 33 papers with code
Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting
Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy.
Data-Centric Strategies for Overcoming PET/CT Heterogeneity: Insights from the AutoPET III Lesion Segmentation Challenge
The third autoPET challenge introduced a new data-centric task this year, shifting the focus from model development to improving metastatic lesion segmentation on PET/CT images through data quality and handling strategies.
From FDG to PSMA: A Hitchhiker's Guide to Multitracer, Multicenter Lesion Segmentation in PET/CT Imaging
Automated lesion segmentation in PET/CT scans is crucial for improving clinical workflows and advancing cancer diagnostics.
Quality Assured: Rethinking Annotation Strategies in Imaging AI
Our work could enable researchers to derive substantially more value from a fixed annotation budget and change the way annotation companies conduct internal QA.
Comparative Benchmarking of Failure Detection Methods in Medical Image Segmentation: Unveiling the Role of Confidence Aggregation
Semantic segmentation is an essential component of medical image analysis research, with recent deep learning algorithms offering out-of-the-box applicability across diverse datasets.
Mitigating False Predictions In Unreasonable Body Regions
This limitation leads to false predictions when applied to body regions beyond the FOV of the training data.
nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation
The release of nnU-Net marked a paradigm shift in 3D medical image segmentation, demonstrating that a properly configured U-Net architecture could still achieve state-of-the-art results.
Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures
Accurately segmenting thin tubular structures, such as vessels, nerves, roads or concrete cracks, is a crucial task in computer vision.
Embarrassingly Simple Scribble Supervision for 3D Medical Segmentation
We relate this shortcoming to two major issues: 1) the complex nature of many methods which deeply ties them to the underlying segmentation model, thus preventing a migration to more powerful state-of-the-art models as the field progresses and 2) the lack of a systematic evaluation to validate consistent performance across the broader medical domain, resulting in a lack of trust when applying these methods to new segmentation problems.
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.
Look Ma, no code: fine tuning nnU-Net for the AutoPET II challenge by only adjusting its JSON plans
We participate in the AutoPET II challenge by modifying nnU-Net only through its easy to understand and modify 'nnUNetPlans. json' file.
RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement
Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision.
Anatomy-informed Data Augmentation for Enhanced Prostate Cancer Detection
Data augmentation (DA) is a key factor in medical image analysis, such as in prostate cancer (PCa) detection on magnetic resonance images.
The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT
Overall KiTS21 facilitated a significant advancement in the state of the art in kidney tumor segmentation, and provides useful insights that are applicable to the field of semantic segmentation as a whole.
Exploring new ways: Enforcing representational dissimilarity to learn new features and reduce error consistency
Given an ensemble of independently trained models, this results in correlated predictions and common failure modes.
Transformer Utilization in Medical Image Segmentation Networks
Owing to success in the data-rich domain of natural images, Transformers have recently become popular in medical image segmentation.
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.
MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation
Our findings offer a new direction for the medical imaging community to effectively utilize the wealth of available data for improved segmentation performance.
MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation
This leads to state-of-the-art performance on 4 tasks on CT and MRI modalities and varying dataset sizes, representing a modernized deep architecture for medical image segmentation.
Ranked #1 on
Medical Image Segmentation
on AMOS
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.
ParticleSeg3D: A Scalable Out-of-the-Box Deep Learning Segmentation Solution for Individual Particle Characterization from Micro CT Images in Mineral Processing and Recycling
Our approach is based on the powerful nnU-Net framework, introduces a particle size normalization, uses a border-core representation to enable instance segmentation, and is trained with a large dataset containing particles of numerous different sizes, shapes, and compositions of various materials.
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization
By utilizing the representations of contrastive learning, we aim to fix the over-fixation on low-level features and learn more semantic-rich representations.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Precise Energy Consumption Measurements of Heterogeneous Artificial Intelligence Workloads
Therefore, accurate measurements of the power draw of AI workflows on different types of compute nodes is key to algorithmic improvements and the design of future compute clusters and hardware.
USE-Evaluator: Performance Metrics for Medical Image Segmentation Models with Uncertain, Small or Empty Reference Annotations
We study how uncertain, small, and empty reference annotations influence the value of metrics for medical image segmentation on an in-house data set regardless of the model.
Extending nnU-Net is all you need
Semantic segmentation is one of the most popular research areas in medical image computing.
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.
Classification of diffraction patterns using a convolutional neural network in single particle imaging experiments performed at X-ray free-electron lasers
Single particle imaging (SPI) at X-ray free electron lasers (XFELs) is particularly well suited to determine the 3D structure of particles in their native environment.
Continuous-Time Deep Glioma Growth Models
The ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation therapy.
How can we learn (more) from challenges? A statistical approach to driving future algorithm development
Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data
Neural Processes (NPs) are a family of conditional generative models that are able to model a distribution over functions, in a way that allows them to perform predictions at test time conditioned on a number of context points.
nnDetection: A Self-configuring Method for Medical Object Detection
Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of high clinical relevance because diagnostic decisions often depend on rating of objects rather than e. g. pixels.
Semantic segmentation of multispectral photoacoustic images using deep learning
Due to the intuitive representation of high-dimensional information, such a preprocessing algorithm could be a valuable means to facilitate the clinical translation of photoacoustic imaging.
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.
Frequency Decomposition in Neural Processes
Neural Processes are a powerful tool for learning representations of function spaces purely from examples, in a way that allows them to perform predictions at test time conditioned on so-called context observations.
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning.
Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI
Cardiac magnetic resonance imaging (cMRI) is an integral part of diagnosis in many heart related diseases.
nnU-Net for Brain Tumor Segmentation
We apply nnU-Net to the segmentation task of the BraTS 2020 challenge.
OR-UNet: an Optimized Robust Residual U-Net for Instrument Segmentation in Endoscopic Images
Segmentation of endoscopic images is an essential processing step for computer and robotics-assisted interventions.
Robust Medical Instrument Segmentation Challenge 2019
The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data.
CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance (DICE: 0. 98 $\pm$ 0. 00 / 0. 95 $\pm$ 0. 01) but the best MSSD performance remain limited (21. 89 $\pm$ 13. 94 / 20. 85 $\pm$ 10. 63 mm).
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge
The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was a competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) which sought to address these issues and stimulate progress on this automatic segmentation problem.
ModelHub.AI: Dissemination Platform for Deep Learning Models
Recent advances in artificial intelligence research have led to a profusion of studies that apply deep learning to problems in image analysis and natural language processing among others.
An attempt at beating the 3D U-Net
The U-Net is arguably the most successful segmentation architecture in the medical domain.
Deep Probabilistic Modeling of Glioma Growth
Existing approaches to modeling the dynamics of brain tumor growth, specifically glioma, employ biologically inspired models of cell diffusion, using image data to estimate the associated parameters.
Unsupervised Anomaly Localization using Variational Auto-Encoders
An assumption-free automatic check of medical images for potentially overseen anomalies would be a valuable assistance for a radiologist.
Automated Design of Deep Learning Methods for Biomedical Image Segmentation
Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning.
Automated brain extraction of multi-sequence MRI using artificial neural networks
Brain extraction is a critical preprocessing step in the analysis of MRI neuroimaging studies and influences the accuracy of downstream analyses.
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.
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection
Unsupervised learning can leverage large-scale data sources without the need for annotations.
Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection
The proposed architecture recaptures discarded supervision signals by complementing object detection with an auxiliary task in the form of semantic segmentation without introducing the additional complexity of previously proposed two-stage detectors.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
No New-Net
By incorporating additional measures such as region based training, additional training data, a simple postprocessing technique and a combination of loss functions, we obtain Dice scores of 77. 88, 87. 81 and 80. 62, and Hausdorff Distances (95th percentile) of 2. 90, 6. 03 and 5. 08 for the enhancing tumor, whole tumor and tumor core, respectively on the test data.
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
The U-Net was presented in 2015.
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge
Quantitative analysis of brain tumors is critical for clinical decision making.
Ranked #2 on
Brain Tumor Segmentation
on BRATS-2015
Exploiting the potential of unlabeled endoscopic video data with self-supervised learning
In this paper, we propose the re-colorization of medical images with a generative adversarial network (GAN)-based architecture as auxiliary task.
Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features
We evaluated our method on the ACDC dataset (4 pathology groups, 1 healthy group) and achieve dice scores of 0. 945 (LVC), 0. 908 (RVC) and 0. 905 (LVM) in a cross-validation over the training set (100 cases) and 0. 950 (LVC), 0. 923 (RVC) and 0. 911 (LVM) on the test set (50 cases).
Direct White Matter Bundle Segmentation using Stacked U-Nets
We present a novel supervised approach for direct tract segmentation that shows major performance gains.
Clickstream analysis for crowd-based object segmentation with confidence
Using a total of 29, 000 crowd annotations performed on publicly available data of different object classes, we show that (1) our method is highly accurate in estimating the segmentation quality based on clickstream data, (2) outperforms state-of-the-art methods for merging multiple annotations.
