Publications (14)
Kidney abnormality segmentation in thorax-abdomen CT scans
Gabriel Efrain Humpire Mamani, Nikolas Lessmann, Ernst Th. Scholten +3
In this study, we introduce a deep learning approach for segmenting kidney parenchyma and kidney abnormalities to support clinicians in identifying and quantifying renal abnormalit…
Unstable Prompts, Unreliable Segmentations: A Challenge for Longitudinal Lesion Analysis
Niels Rocholl, Ewoud Smit, Mathias Prokop +1
Longitudinal lesion analysis is crucial for oncological care, yet automated tools often struggle with temporal consistency. While universal lesion segmentation models have advanced…
Transfer learning from a sparsely annotated dataset of 3D medical images
Gabriel Efrain Humpire-Mamani, Colin Jacobs, Mathias Prokop +2
Transfer learning leverages pre-trained model features from a large dataset to save time and resources when training new models for various tasks, potentially enhancing performance…
Robust Renal Mass Segmentation on CT: A Validation Study of an AI-Based Framework
Sarah de Boer, Hartmut Häntze, Kiran Vaidhya Venkadesh +9
Renal mass segmentation has important potential to enhance the clinical workflow, especially in settings requiring quantitative assessments. Kidney volume could serve as an importa…
Divide to Conquer: A Field Decomposition Approach for Multi-Organ Whole-Body CT Image Registration
Xuan Loc Pham, Mathias Prokop, Bram van Ginneken +1
Image registration is an essential technique for the analysis of Computed Tomography (CT) images in clinical practice. However, existing methodologies are predominantly tailored to…
Automated Estimation of Total Lung Volume using Chest Radiographs and Deep Learning
Ecem Sogancioglu, Keelin Murphy, Ernst Th. Scholten +3
Total lung volume is an important quantitative biomarker and is used for the assessment of restrictive lung diseases. In this study, we investigate the performance of several deep-…
Benchmarking Foundation Models for Renal Lesion Stratification in CT
Hartmut Häntze, Sarah de Boer, Myrthe Buser +7
The rapid proliferation of open-source medical foundation models (FMs) raises a practical question: how well do their pre-trained representations transfer to clinically relevant bu…
ULS+: Data-driven Model Adaptation Enhances Lesion Segmentation
Rianne Weber, Niels Rocholl, Max de Grauw +3
In this study, we present ULS+, an enhanced version of the Universal Lesion Segmentation (ULS) model. The original ULS model segments lesions across the whole body in CT scans give…
MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
Hartmut Häntze, Lina Xu, Christian J. Mertens +29
Purpose: To develop and evaluate a deep learning model for multi-organ segmentation of MRI scans. Materials and Methods: The model was trained on 1,200 manually annotated 3D axial…
Designing UNICORN: a Unified Benchmark for Imaging in Computational Pathology, Radiology, and Natural Language
Michelle Stegeman, Lena Philipp, Fennie van der Graaf +19
Medical foundation models show promise to learn broadly generalizable features from large, diverse datasets. This could be the base for reliable cross-modality generalization and r…
TotalRegistrator: Towards a Lightweight Foundation Model for CT Image Registration
Xuan Loc Pham, Gwendolyn Vuurberg, Marjan Doppen +12
Image registration is a fundamental technique in the analysis of longitudinal and multi-phase CT images within clinical practice. However, most existing methods are tailored for si…
Towards automatic pulmonary nodule management in lung cancer screening with deep learning
Francesco Ciompi, Kaman Chung, Sarah J. van Riel +10
The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decid…
Leveraging Open-Source Large Language Models for Clinical Information Extraction in Resource-Constrained Settings
Luc Builtjes, Joeran Bosma, Mathias Prokop +2
Medical reports contain rich clinical information but are often unstructured and written in domain-specific language, posing challenges for information extraction. While proprietar…
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge
Arnaud Arindra Adiyoso Setio, Alberto Traverso, Thomas de Bel +29
Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few…