1.2k citations · 1.9k across the 32 of their papers we have counts for
35 papers
VERIDAH: Solving Enumeration Anomaly Aware Vertebra Labeling across Imaging Sequences
Hendrik Möller, Hanna Schoen, Robert Graf +14
The human spine commonly consists of seven cervical, twelve thoracic, and five lumbar vertebrae. However, enumeration anomalies may result in individuals having eleven or thirteen…
RadDiagSeg-M: A Vision Language Model for Joint Diagnosis and Multi-Target Segmentation in Radiology
Chengrun Li, Corentin Royer, Haozhe Luo +6
Most current medical vision language models struggle to jointly generate diagnostic text and pixel-level segmentation masks in response to complex visual questions. This represents…
CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography
Murong Xu, Tamaz Amiranashvili, Fernando Navarro +30
Accurate delineation of anatomical structures in volumetric CT scans is crucial for diagnosis and treatment planning. While AI has advanced automated segmentation, current approach…
DeViDe: Faceted medical knowledge for improved medical vision-language pre-training
Haozhe Luo, Ziyu Zhou, Corentin Royer +2
Vision-language pre-training for chest X-rays has made significant strides, primarily by utilizing paired radiographs and radiology reports. However, existing approaches often face…
Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes
Poulami Sinhamahapatra, Suprosanna Shit, Anjany Sekuboyina +7
Vertebral fracture grading classifies the severity of vertebral fractures, which is a challenging task in medical imaging and has recently attracted Deep Learning (DL) models. Only…
Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography
Ibrahim Ethem Hamamci, Sezgin Er, Chenyu Wang +27
Advancements in medical imaging AI, particularly in 3D imaging, have been limited due to the scarcity of comprehensive datasets. We introduce CT-RATE, a public dataset that pairs 3…
SPINEPS -- Automatic Whole Spine Segmentation of T2-weighted MR images using a Two-Phase Approach to Multi-class Semantic and Instance Segmentation
Hendrik Möller, Robert Graf, Joachim Schmitt +16
Purpose. To present SPINEPS, an open-source deep learning approach for semantic and instance segmentation of 14 spinal structures (ten vertebra substructures, intervertebral discs,…
MultiMedEval: A Benchmark and a Toolkit for Evaluating Medical Vision-Language Models
Corentin Royer, Bjoern Menze, Anjany Sekuboyina
We introduce MultiMedEval, an open-source toolkit for fair and reproducible evaluation of large, medical vision-language models (VLM). MultiMedEval comprehensively assesses the mod…
The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography
Kaiyuan Yang, Fabio Musio, Yihui Ma +112
The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to influence the risk, severity, an…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation
Robert Graf, Joachim Schmitt, Sarah Schlaeger +8
Background: Automated segmentation of spinal MR images plays a vital role both scientifically and clinically. However, accurately delineating posterior spine structures presents ch…
GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes
Ibrahim Ethem Hamamci, Sezgin Er, Anjany Sekuboyina +13
GenerateCT, the first approach to generating 3D medical imaging conditioned on free-form medical text prompts, incorporates a text encoder and three key components: a novel causal…
Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays
Ibrahim Ethem Hamamci, Sezgin Er, Enis Simsar +5
Due to the necessity for precise treatment planning, the use of panoramic X-rays to identify different dental diseases has tremendously increased. Although numerous ML models have…
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)
Johannes C. Paetzold, Julian McGinnis, Suprosanna Shit +9
Biological neural networks define the brain function and intelligence of humans and other mammals, and form ultra-large, spatial, structured graphs. Their neuronal organization is…
Evaluating the Robustness of Self-Supervised Learning in Medical Imaging
Fernando Navarro, Christopher Watanabe, Suprosanna Shit +4
Self-supervision has demonstrated to be an effective learning strategy when training target tasks on small annotated data-sets. While current research focuses on creating novel pre…
The MICCAI Hackathon on reproducibility, diversity, and selection of papers at the MICCAI conference
Fabian Balsiger, Alain Jungo, Naren Akash R J +12
The MICCAI conference has encountered tremendous growth over the last years in terms of the size of the community, as well as the number of contributions and their technical succes…
Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning
Christina Bukas, Bailiang Jian, Luis F. Rodriguez Venegas +9
Symptomatic spinal vertebral compression fractures (VCFs) often require osteoplasty treatment. A cement-like material is injected into the bone to stabilize the fracture, restore t…
A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data
Hans Liebl, David Schinz, Anjany Sekuboyina +13
With the advent of deep learning algorithms, fully automated radiological image analysis is within reach. In spine imaging, several atlas- and shape-based as well as deep learning…
A Relational-learning Perspective to Multi-label Chest X-ray Classification
Anjany Sekuboyina, Daniel Oñoro-Rubio, Jens Kleesiek +1
Multi-label classification of chest X-ray images is frequently performed using discriminative approaches, i.e. learning to map an image directly to its binary labels. Such approach…
Micro-CT Synthesis and Inner Ear Super Resolution via Generative Adversarial Networks and Bayesian Inference
Hongwei Li, Rameshwara G. N. Prasad, Anjany Sekuboyina +4
Existing medical image super-resolution methods rely on pairs of low- and high- resolution images to learn a mapping in a fully supervised manner. However, such image pairs are oft…
Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection
Malek Husseini, Anjany Sekuboyina, Maximilian Loeffler +3
Osteoporotic vertebral fractures have a severe impact on patients' overall well-being but are severely under-diagnosed. These fractures present themselves at various levels of seve…
Inferring the 3D Standing Spine Posture from 2D Radiographs
Amirhossein Bayat, Anjany Sekuboyina, Johannes C. Paetzold +5
The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3D. An upright spinal pose (i.e. standing) under natural…
Deep Reinforcement Learning for Organ Localization in CT
Fernando Navarro, Anjany Sekuboyina, Diana Waldmannstetter +3
Robust localization of organs in computed tomography scans is a constant pre-processing requirement for organ-specific image retrieval, radiotherapy planning, and interventional im…
Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting
Carolin M. Pirkl, Pedro A. Gómez, Ilona Lipp +13
Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues. It relies on a pseudo-random acquisition and the match…