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Found 59 papers, 12 papers with code
Deep Medial Voxels: Learned Medial Axis Approximations for Anatomical Shape Modeling
Shape reconstruction from imaging volumes is a recurring need in medical image analysis.
How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation
Deep Learning is the state-of-the-art technology for segmenting brain tumours.
Deep PCCT: Photon Counting Computed Tomography Deep Learning Applications Review
This review delves into the recent developments and applications of PCCT in pre-clinical research, emphasizing its potential to overcome traditional imaging limitations.
Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images
Utilizing this dataset, we compared the performance of Cyto R-CNN to other popular cell segmentation algorithms, including QuPath's built-in algorithm, StarDist and Cellpose.
DeepDR: Deep Structure-Aware RGB-D Inpainting for Diminished Reality
Diminished reality (DR) refers to the removal of real objects from the environment by virtually replacing them with their background.
Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy
Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback.
On the Impact of Cross-Domain Data on German Language Models
Traditionally, large language models have been either trained on general web crawls or domain-specific data.
Multilingual Natural Language Processing Model for Radiology Reports -- The Summary is all you need!
In this study, the generation of radiology impressions in different languages was automated by fine-tuning a model, publicly available, based on a multilingual text-to-text Transformer to summarize findings available in English, Portuguese, and German radiology reports.
Anatomy Completor: A Multi-class Completion Framework for 3D Anatomy Reconstruction
We propose two paradigms based on a 3D denoising auto-encoder (DAE) to solve the anatomy reconstruction problem: (i) the DAE learns a many-to-one mapping between incomplete and complete instances; (ii) the DAE learns directly a one-to-one residual mapping between the incomplete instances and the target anatomies.
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.
Classification of lung cancer subtypes on CT images with synthetic pathological priors
The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements.
Apple Vision Pro for Healthcare: "The Ultimate Display"? -- Entering the Wonderland of Precision Medicine
At the Worldwide Developers Conference (WWDC) in June 2023, Apple introduced the Vision Pro.
FAM: Relative Flatness Aware Minimization
Their widespread adoption in practice, though, is dubious because of the lack of theoretically grounded connection between flatness and generalization, in particular in light of the reparameterization curse - certain reparameterizations of a neural network change most flatness measures but do not change generalization.
Why does my medical AI look at pictures of birds? Exploring the efficacy of transfer learning across domain boundaries
It is an open secret that ImageNet is treated as the panacea of pretraining.
CellViT: Vision Transformers for Precise Cell Segmentation and Classification
Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications.
Ranked #1 on
Cell Detection
on PanNuke
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.
Open-Source Skull Reconstruction with MONAI
The primary goal of this paper lies in the investigation of open-sourcing codes and pre-trained deep learning models under the MONAI framework.
'A net for everyone': fully personalized and unsupervised neural networks trained with longitudinal data from a single patient
Using this map, the change in tumor volume can be evaluated.
Valuing Vicinity: Memory attention framework for context-based semantic segmentation in histopathology
The segmentation of histopathological whole slide images into tumourous and non-tumourous types of tissue is a challenging task that requires the consideration of both local and global spatial contexts to classify tumourous regions precisely.
Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder
The reconstruction loss and the Kullback-Leibler divergence (KLD) loss in a variational autoencoder (VAE) often play antagonistic roles, and tuning the weight of the KLD loss in $\beta$-VAE to achieve a balance between the two losses is a tricky and dataset-specific task.
The HoloLens in Medicine: A systematic Review and Taxonomy
The HoloLens (Microsoft Corp., Redmond, WA), a head-worn, optically see-through augmented reality display, is the main player in the recent boost in medical augmented reality research.
AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection Classifier
Tumor volume and changes in tumor characteristics over time are important biomarkers for cancer therapy.
FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomy
In this review, we provide a summary of works that generate realistic volumetric synthetic data using GANs.
k-strip: A novel segmentation algorithm in k-space for the application of skull stripping
Results: Both datasets were very similar to the ground truth (DICE scores of 92\%-98\% and Hausdorff distances of under 5. 5 mm).
Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model
We evaluate the SSM on several cranial implant design tasks, and the results show that, while the SSM performs suboptimally on synthetic defects compared to CNN-based approaches, it is capable of reconstructing large and complex defects with only minor manual corrections.
Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare
Encouraging the latent representation of a generative model to be disentangled offers new perspectives of control and interpretability.
Stochastic Modeling of Inhomogeneities in the Aortic Wall and Uncertainty Quantification using a Bayesian Encoder-Decoder Surrogate
Then to assess the uncertainty in the output stress distribution due to this stochastic constitutive model, a convolutional neural network, specifically a Bayesian encoder-decoder, was used as a surrogate model that maps the random input fields to the output stress distribution obtained from the FE analysis.
MOMO -- Deep Learning-driven classification of external DICOM studies for PACS archivation
Additionally, an ablation study was performed to measure the performance impact of the network ensemble in the algorithm, and a comparative performance test with a commercial product was conducted.
Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations
Conclusion: This suggests that pre-existing annotations can be an inexpensive resource in clinics to ease ill-posed and repetitive tasks like cross-section extraction for surveillance of aortic dissections.
Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation
However, downsampling can lead to a loss of image quality, which is undesirable especially in reconstruction tasks, where the fine geometric details need to be preserved.
AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo
The standard imaging modality for diagnosis and monitoring is computed tomography (CT), which can provide a detailed picture of the aorta and its branching vessels if completed with a contrast agent, called CT angiography (CTA).
Deep Learning -- A first Meta-Survey of selected Reviews across Scientific Disciplines, their Commonalities, Challenges and Research Impact
These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years.
Medical Deep Learning -- A systematic Meta-Review
With the collection of large quantities of patient records and data, and a trend towards personalized treatments, there is a great need for automated and reliable processing and analysis of health information.
A Baseline Approach for AutoImplant: the MICCAI 2020 Cranial Implant Design Challenge
The approach generates high-quality implants in two steps: First, an encoder-decoder network learns a coarse representation of the implant from down-sampled, defective skulls; The coarse implant is only used to generate the bounding box of the defected region in the original high-resolution skull.
An Online Platform for Automatic Skull Defect Restoration and Cranial Implant Design
Such an automatic cranial implant design system can be integrated into the clinical practice to improve the current routine for surgeries related to skull defect repair (e. g., cranioplasty).
Design and Development of a Web-based Tool for Inpainting of Dissected Aortae in Angiography Images
Medical imaging is an important tool for the diagnosis and the evaluation of an aortic dissection (AD); a serious condition of the aorta, which could lead to a life-threatening aortic rupture.
Client/Server Based Online Environment for Manual Segmentation of Medical Images
The user study resulted in a mean of 6. 3 out of 7. 0 possible points given by users, when asked about their overall impression of the tool.
Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data
Since deep neural networks require a large amount of training data, specifically images and corresponding ground truth labels, we furthermore propose a method to generate such a suitable training data set from Positron Emission Tomography/Computed Tomography image data.
Computed tomography data collection of the complete human mandible and valid clinical ground truth models
Inaccuracy or invalidity of medical ground truth data and image-based artefacts also limit the creation of such databases, which is especially relevant for CT data sets of the maxillomandibular complex.
Clinical evaluation of semi-automatic opensource algorithmic software segmentation of the mandibular bone: Practical feasibility and assessment of a new course of action
Therefore, the aim of this trial was to assess the practical feasibility of an easy available, functional stable and licensed-free segmentation approach to be used in the clinical practice.
In-depth Assessment of an Interactive Graph-based Approach for the Segmentation for Pancreatic Metastasis in Ultrasound Acquisitions of the Liver with two Specialists in Internal Medicine
Ultimately, the algorithm enables a fast and accurate segmentation of liver metastasis in clinical US images, which can support the manual outlining in daily practice.
Vertebral body segmentation with GrowCut: Initial experience, workflow and practical application
In this contribution, we used the GrowCut segmentation algorithm publicly available in three-dimensional Slicer for three-dimensional segmentation of vertebral bodies.
Algorithm guided outlining of 105 pancreatic cancer liver metastases in Ultrasound
In this work, we present the application of an algorithm for the segmentation of liver metastases due to pancreatic cancer using a set of 105 different images of metastases.
Computer-aided position planning of miniplates to treat facial bone defects
In this contribution, a software system for computer-aided position planning of miniplates to treat facial bone defects is proposed.
Computer-aided implant design for the restoration of cranial defects
Patient-specific cranial implants are important and necessary in the surgery of cranial defect restoration.
Interactive Outlining of Pancreatic Cancer Liver Metastases in Ultrasound Images
In this contribution, we present the results of an interactive segmentation approach for liver metastases in US acquisitions.
Cellular Automata Segmentation of the Boundary between the Compacta of Vertebral Bodies and Surrounding Structures
Due to the aging population, spinal diseases get more and more common nowadays; e. g., lifetime risk of osteoporotic fracture is 40% for white women and 13% for white men in the United States.
Interactive and Scale Invariant Segmentation of the Rectum/Sigmoid via User-Defined Templates
Evaluation was performed by comparing manual segmentations of the rectum/sigmoid colon to results achieved with the proposed method.
US-Cut: Interactive Algorithm for rapid Detection and Segmentation of Liver Tumors in Ultrasound Acquisitions
We present an interactive segmentation approach for liver tumors in US acquisitions.
Detection and Visualization of Endoleaks in CT Data for Monitoring of Thoracic and Abdominal Aortic Aneurysm Stents
The aneurysm segmentation includes two steps: first, the inner boundary is segmented based on a grey level model with two thresholds; then, an adapted active contour model approach is applied to the more complicated outer boundary segmentation, with its initialization based on the available inner boundary segmentation.
Preoperative Volume Determination for Pituitary Adenoma
The most common sellar lesion is the pituitary adenoma, and sellar tumors are approximately 10-15% of all intracranial neoplasms.
A semi-automatic computer-aided method for surgical template design
Then, the offset surface of the inner surface is obtained through contouring the distance field of the inner surface, and segmented to generate the outer surface.
Interactive Volumetry Of Liver Ablation Zones
The visual feedback and interactivity make the proposed tool well suitable for the clinical workflow.
Refinement-Cut: User-Guided Segmentation Algorithm for Translational Science
I propose a solution where the user can support the algorithm by an easy and fast placement of one or more seed points to guide the algorithm to a satisfying segmentation result also in difficult cases.
Cube-Cut: Vertebral Body Segmentation in MRI-Data through Cubic-Shaped Divergences
The Cube-Cut algorithm generates a directed graph with two terminal nodes (s-t-network), where the nodes of the graph correspond to a cubic-shaped subset of the image's voxels.
A Ray-based Approach for Boundary Estimation of Fiber Bundles Derived from Diffusion Tensor Imaging
Using DTI data, fiber bundles can be determined, to gain information about eloquent brain structures.
Determination, Calculation and Representation of the Upper and Lower Sealing Zones During Virtual Stenting of Aneurysms
In this contribution, a novel method for stent simulation in preoperative computed tomography angiography (CTA) acquisitions of patients is presented where the sealing zones are automatically calculated and visualized.
PCG-Cut: Graph Driven Segmentation of the Prostate Central Gland
The aim of this research study is to apply a graph-based algorithm to automated segmentation (i. e. delineation) of organ limits for the prostate central gland.
