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Found 27 papers, 9 papers with code
3-D Image-to-Image Fusion in Lightsheet Microscopy by Two-Step Adversarial Network: Contribution to the FuseMyCells Challenge
Lightsheet microscopy is a powerful 3-D imaging technique that addresses limitations of traditional optical and confocal microscopy but suffers from a low penetration depth and reduced image quality at greater depths.
Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge
Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods.
Automatic Skull Reconstruction by Deep Learnable Symmetry Enforcement
The proposed method is a considerable contribution to the field of applied artificial intelligence in medicine and is a step toward automatic cranial defect reconstruction in clinical practice.
Unsupervised Skull Segmentation via Contrastive MR-to-CT Modality Translation
The skull segmentation from CT scans can be seen as an already solved problem.
Automatic Registration of SHG and H&E Images with Feature-based Initial Alignment and Intensity-based Instance Optimization: Contribution to the COMULIS Challenge
The automatic registration of noninvasive second-harmonic generation microscopy to hematoxylin and eosin slides is a highly desired, yet still unsolved problem.
Automatic Labels are as Effective as Manual Labels in Biomedical Images Classification with Deep Learning
The increasing availability of biomedical data is helping to design more robust deep learning (DL) algorithms to analyze biomedical samples.
Improving Quality Control of Whole Slide Images by Explicit Artifact Augmentation
The problem of artifacts in whole slide image acquisition, prevalent in both clinical workflows and research-oriented settings, necessitates human intervention and re-scanning.
Improving Deep Learning-based Automatic Cranial Defect Reconstruction by Heavy Data Augmentation: From Image Registration to Latent Diffusion Models
The modeling of personalized implants may be partially automated by the use of deep learning-based methods.
Patch-Based Encoder-Decoder Architecture for Automatic Transmitted Light to Fluorescence Imaging Transition: Contribution to the LightMyCells Challenge
Automatic prediction of fluorescently labeled organelles from label-free transmitted light input images is an important, yet difficult task.
DeeperHistReg: Robust Whole Slide Images Registration Framework
DeeperHistReg is a software framework dedicated to registering whole slide images (WSIs) acquired using multiple stains.
Eye-tracking in Mixed Reality for Diagnosis of Neurodegenerative Diseases
Parkinson's disease ranks as the second most prevalent neurodegenerative disorder globally.
Automatic Cranial Defect Reconstruction with Self-Supervised Deep Deformable Masked Autoencoders
However, even the synthetic ground-truth generation is time-consuming and limits the data heterogeneity, thus the deep models' generalizability.
RegWSI: Whole Slide Image Registration using Combined Deep Feature- and Intensity-Based Methods: Winner of the ACROBAT 2023 Challenge
The proposed method does not require any fine-tuning to a particular dataset and can be used directly for any desired tissue type and stain.
Deep Learning-Based Segmentation of Tumors in PET/CT Volumes: Benchmark of Different Architectures and Training Strategies
The results for the HECKTOR dataset ranged from 0. 75 to 0. 76 for the aggregated Dice coefficient.
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.
Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D ResUNet: Contribution to the SEG.A Challenge
We propose a fully automated algorithm based on deep encoder-decoder architecture.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
Deep Generative Networks for Heterogeneous Augmentation of Cranial Defects
We show that it is possible to generate dozens of thousands of defective skulls with compatible defects that achieve a trade-off between defect heterogeneity and the realistic shape of the skull.
High-Resolution Cranial Defect Reconstruction by Iterative, Low-Resolution, Point Cloud Completion Transformers
The problem of the automatic cranial defect reconstruction can be formulated as the shape completion task and solved using dedicated deep networks.
The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue
The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications.
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.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Unsupervised Method for Intra-patient Registration of Brain Magnetic Resonance Images based on Objective Function Weighting by Inverse Consistency: Contribution to the BraTS-Reg Challenge
The importance of this task motivated researchers to organize the BraTS-Reg challenge, jointly with IEEE ISBI 2022 and MICCAI 2022 conferences.
Deep Learning-based Framework for Automatic Cranial Defect Reconstruction and Implant Modeling
The goal of this work is to propose a robust, fast, and fully automatic method for personalized cranial defect reconstruction and implant modeling.
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
Automatic Nonrigid Histological Image Registration with Adaptive Multistep Algorithm
In this paper, we present a short description of the method proposed to ANHIR challenge organized jointly with the IEEE ISBI 2019 conference.
