activity
20162026
most citedThe Liver Tumor Segmentation Benchmark (LiTS)

1.2k citations · 1.9k across the 32 of their papers we have counts for

collaborators
Showing eess.IVShow all

10 papers · 1 filter

eess.IV2025

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…

eess.IV2024★ 28 cited

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,…

eess.IV2023★ 44 cited

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…

eess.IV2021★ 1 cited

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…

eess.IV2020

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…

eess.IV2020

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…

eess.IV2020

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…

eess.IV2020★ 24 cited

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…

eess.IV2020★ 3 cited

Red-GAN: Attacking class imbalance via conditioned generation. Yet another perspective on medical image synthesis for skin lesion dermoscopy and brain tumor MRI

Ahmad B Qasim, Ivan Ezhov, Suprosanna Shit +7

Exploiting learning algorithms under scarce data regimes is a limitation and a reality of the medical imaging field. In an attempt to mitigate the problem, we propose a data augmen…

eess.IV2019

Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysis

Anjany Sekuboyina, Markus Rempfler, Alexander Valentinitsch +3

We propose an auto-encoding network architecture for point clouds (PC) capable of extracting shape signatures without supervision. Building on this, we (i) design a loss function c…