Zobrazeno 1 - 10
of 282
pro vyhledávání: '"Heinrich Mattias P."'
Autor:
Heinrich, Mattias Paul
Point clouds are a very efficient way to represent volumetric data in medical imaging. First, they do not occupy resources for empty spaces and therefore can avoid trade-offs between resolution and field-of-view for voxel-based 3D convolutional netwo
Externí odkaz:
http://arxiv.org/abs/2412.17390
Fractures, particularly in the distal forearm, are among the most common injuries in children and adolescents, with approximately 800 000 cases treated annually in Germany. The AO/OTA system provides a structured fracture type classification, which s
Externí odkaz:
http://arxiv.org/abs/2412.13856
Autor:
Keuth, Ron, Hansen, Lasse, Balks, Maren, Jäger, Ronja, Schröder, Anne-Nele, Tüshaus, Ludger, Heinrich, Mattias
Semantic segmentation is a crucial task in medical imaging. Although supervised learning techniques have proven to be effective in performing this task, they heavily depend on large amounts of annotated training data. The recently introduced Segment
Externí odkaz:
http://arxiv.org/abs/2411.12602
Autor:
Bockelmann Niclas, Graßhoff Jan, Hansen Lasse, Bellani Giacomo, Heinrich Mattias P., Rostalski Philipp
Publikováno v:
Current Directions in Biomedical Engineering, Vol 5, Iss 1, Pp 17-20 (2019)
The electrical activity of the diaphragm (EAdi) is a novel monitoring parameter for patients under assisted ventilation and is used for assessing the patient’s neural respiratory drive. It is recorded by an array of electrodes placed inside the eso
Externí odkaz:
https://doaj.org/article/13a0b43939d44e58b96eb18f93a1cddf
Deformable image registration is fundamental to many medical imaging applications. Registration is an inherently ambiguous task often admitting many viable solutions. While neural network-based registration techniques enable fast and accurate registr
Externí odkaz:
http://arxiv.org/abs/2407.10567
Motion artifacts in Magnetic Resonance Imaging (MRI) arise due to relatively long acquisition times and can compromise the clinical utility of acquired images. Traditional motion correction methods often fail to address severe motion, leading to dist
Externí odkaz:
http://arxiv.org/abs/2407.02974
Autor:
Keuth, Ron, Hansen, Lasse, Balks, Maren, Jäger, Ronja, Schröder, Anne-Nele, Tüshaus, Ludger, Heinrich, Mattias
Purpose: Semantic segmentation and landmark detection are fundamental tasks of medical image processing, facilitating further analysis of anatomical objects. Although deep learning-based pixel-wise classification has set a new-state-of-the-art for se
Externí odkaz:
http://arxiv.org/abs/2405.19746
Publikováno v:
Current Directions in Biomedical Engineering, Vol 4, Iss 1, Pp 297-300 (2018)
Low-dose CT has received increasing attention in the recent years and is considered a promising method to reduce the risk of cancer in patients. However, the reduction of the dosage leads to quantum noise in the raw data, which is carried on in the r
Externí odkaz:
https://doaj.org/article/6bfc142c87e0464185c5555e4b194377
Autor:
Hering, Alessa, de Boer, Sarah, Saha, Anindo, Twilt, Jasper J., Heinrich, Mattias P., Yakar, Derya, de Rooij, Maarten, Huisman, Henkjan, Bosma, Joeran S.
The PI-CAI (Prostate Imaging: Cancer AI) challenge led to expert-level diagnostic algorithms for clinically significant prostate cancer detection. The algorithms receive biparametric MRI scans as input, which consist of T2-weighted and diffusion-weig
Externí odkaz:
http://arxiv.org/abs/2404.09666
Autor:
Keuth, Ron, Heinrich, Mattias
When solving a segmentation task, shaped-base methods can be beneficial compared to pixelwise classification due to geometric understanding of the target object as shape, preventing the generation of anatomical implausible predictions in particular f
Externí odkaz:
http://arxiv.org/abs/2401.07542