Thilo Sentker
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View article: Fully automated quantification of net water uptake in acute ischemic stroke using only non-contrast CT imaging
Fully automated quantification of net water uptake in acute ischemic stroke using only non-contrast CT imaging Open
Objective Estimating early lesion progression in ischemic stroke is essential for assessing thrombolytic treatment efficacy. While computed tomography perfusion (CTP) and diffusion-weighted imaging (DWI) are commonly used to determine irre…
View article: Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges
Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges Open
Medical image registration is critical for clinical applications, and fair benchmarking of different methods is essential for monitoring ongoing progress. To date, the Learn2Reg 2020-2023 challenges have released several complementary data…
View article: Beyond the LUMIR challenge: The pathway to foundational registration models
Beyond the LUMIR challenge: The pathway to foundational registration models Open
Medical image challenges have played a transformative role in advancing the field, catalyzing algorithmic innovation and establishing new performance standards across diverse clinical applications. Image registration, a foundational task i…
View article: Oriented histogram-based vector field embedding for characterizing 4D CT data sets in radiotherapy
Oriented histogram-based vector field embedding for characterizing 4D CT data sets in radiotherapy Open
In lung radiotherapy, the primary objective is to optimize treatment outcomes by minimizing exposure to healthy tissues while delivering the prescribed dose to the target volume. The challenge lies in accounting for lung tissue motion due …
View article: Monte Carlo-based simulation of virtual 3 and 4-dimensional cone-beam computed tomography from computed tomography images: An end-to-end framework and a deep learning-based speedup strategy
Monte Carlo-based simulation of virtual 3 and 4-dimensional cone-beam computed tomography from computed tomography images: An end-to-end framework and a deep learning-based speedup strategy Open
The presented MC pipeline and speedup approach provide an openly accessible end-to-end framework for researchers and clinicians to investigate limitations of image-guided radiation therapy workflows built on both (4D) CT and CBCT images.
View article: Impact of breathing signal‐guided dose modulation on step‐and‐shoot 4D CT image reconstruction
Impact of breathing signal‐guided dose modulation on step‐and‐shoot 4D CT image reconstruction Open
Background Breathing signal‐guided 4D CT sequence scanning such as the intelligent 4D CT (i4DCT) approach reduces imaging artifacts compared to conventional 4D CT. By design, i4DCT captures entire breathing cycles during beam‐on periods, l…
View article: Deep learning‐based conditional inpainting for restoration of artifact‐affected 4D CT images
Deep learning‐based conditional inpainting for restoration of artifact‐affected 4D CT images Open
Background 4D CT imaging is an essential component of radiotherapy of thoracic and abdominal tumors. 4D CT images are, however, often affected by artifacts that compromise treatment planning quality and image information reliability. Purpo…
View article: Self-Supervision for Medical Image Classification: State-of-the-Art Performance with ~100 Labeled Training Samples per Class
Self-Supervision for Medical Image Classification: State-of-the-Art Performance with ~100 Labeled Training Samples per Class Open
Is self-supervised deep learning (DL) for medical image analysis already a serious alternative to the de facto standard of end-to-end trained supervised DL? We tackle this question for medical image classification, with a particular focus …
View article: Intuitive Surgical SurgToolLoc Challenge Results: 2022-2023
Intuitive Surgical SurgToolLoc Challenge Results: 2022-2023 Open
Robotic assisted (RA) surgery promises to transform surgical intervention. Intuitive Surgical is committed to fostering these changes and the machine learning models and algorithms that will enable them. With these goals in mind we have in…
View article: Self-supervision for medical image classification: state-of-the-art performance with ~100 labeled training samples per class
Self-supervision for medical image classification: state-of-the-art performance with ~100 labeled training samples per class Open
Is self-supervised deep learning (DL) for medical image analysis already a serious alternative to the de facto standard of end-to-end trained supervised DL? We tackle this question for medical image classification, with a particular focus …
View article: Clinical application of breathing-adapted 4D CT: image quality comparison to conventional 4D CT
Clinical application of breathing-adapted 4D CT: image quality comparison to conventional 4D CT Open
Purpose: 4D CT imaging is an integral part of 4D radiotherapy workflows. However, 4D CT data often contain motion artifacts that mitigate treatment planning. Recently, breathing-adapted 4D CT (i4DCT) was introduced into clinical practice, …
View article: P94 Deep learning-based automated device detection for assessment standardisation in mechanical thrombectomy
P94 Deep learning-based automated device detection for assessment standardisation in mechanical thrombectomy Open
Introduction Clinical benefits of mechanical thrombectomy (MT) with stent retriever (SR) has shown irrefutable evidence. Post-operative reconstruction of procedural steps is due to lack of conformity in image documentation often challengin…
View article: Deep Learning–Based Automated Thrombolysis in Cerebral Infarction Scoring: A Timely Proof-of-Principle Study
Deep Learning–Based Automated Thrombolysis in Cerebral Infarction Scoring: A Timely Proof-of-Principle Study Open
Background and Purpose: Mechanical thrombectomy is an established procedure for treatment of acute ischemic stroke. Mechanical thrombectomy success is commonly assessed by the Thrombolysis in Cerebral Infarction (TICI) score, assigned by v…
View article: Self‐contained deep learning‐based boosting of 4D cone‐beam CT reconstruction
Self‐contained deep learning‐based boosting of 4D cone‐beam CT reconstruction Open
Purpose Four‐dimensional cone‐beam computed tomography (4D CBCT) imaging has been suggested as a solution to account for interfraction motion variability of moving targets like lung and liver during radiotherapy (RT) of moving targets. How…
View article: Intelligent 4D CT sequence scanning (i4DCT): First scanner prototype implementation and phantom measurements of automated breathing signal‐guided 4D CT
Intelligent 4D CT sequence scanning (i4DCT): First scanner prototype implementation and phantom measurements of automated breathing signal‐guided 4D CT Open
Purpose Four‐dimensional (4D) computed tomography (CT) imaging is an essential part of current 4D radiotherapy treatment planning workflows, but clinical 4D CT images are often affected by artifacts. The artifacts are mainly caused by brea…
View article: Skin Lesion Classification Using CNNs With Patch-Based Attention and Diagnosis-Guided Loss Weighting
Skin Lesion Classification Using CNNs With Patch-Based Attention and Diagnosis-Guided Loss Weighting Open
The proposed methods improve automatic skin lesion classification. They can be extended to other clinical applications where high-resolution image data and class imbalance are relevant.
View article: 4D cone beam computed tomography phantom data set
4D cone beam computed tomography phantom data set Open
This data set accompanies the following Medical Physics publication: Madesta, F., Sentker, T., Gauer, T., & Werner, R. (2020). Self‐contained deep learning‐based boosting of 4D cone‐beam CT reconstruction. Medical Physics, 47(11), 5619-563…
View article: 4D cone beam computed tomography phantom data set
4D cone beam computed tomography phantom data set Open
This data set accompanies the following Medical Physics publication: Madesta, F., Sentker, T., Gauer, T., & Werner, R. (2020). Self‐contained deep learning‐based boosting of 4D cone‐beam CT reconstruction. Medical Physics, 47(11), 5619-563…
View article: Skin Lesion Diagnosis using Ensembles, Unscaled Multi-Crop Evaluation and Loss Weighting
Skin Lesion Diagnosis using Ensembles, Unscaled Multi-Crop Evaluation and Loss Weighting Open
In this paper we present the methods of our submission to the ISIC 2018 challenge for skin lesion diagnosis (Task 3). The dataset consists of 10000 images with seven image-level classes to be distinguished by an automated algorithm. We emp…