no code implementations • 23 Apr 2024 • Siyuan Mei, Fuxin Fan, Mareike Thies, Mingxuan Gu, Fabian Wagner, Oliver Aust, Ina Erceg, Zeynab Mirzaei, Georgiana Neag, Yipeng Sun, Yixing Huang, Andreas Maier
Recently, X-ray microscopy (XRM) and light-sheet fluorescence microscopy (LSFM) have emerged as two pivotal imaging tools in preclinical research on bone remodeling diseases, offering micrometer-level resolution.
1 code implementation • 15 Mar 2024 • Yipeng Sun, Yixing Huang, Linda-Sophie Schneider, Mareike Thies, Mingxuan Gu, Siyuan Mei, Siming Bayer, Andreas Maier
However, the choice of loss function profoundly affects the reconstructed images.
no code implementations • 23 Jan 2024 • Chang Liu, Laura Klein, Yixing Huang, Edith Baader, Michael Lell, Marc Kachelrieß, Andreas Maier
The average organ dice of the proposed method is 0. 71 compared with 0. 63 in baseline model, indicating the enhancement of anatomical structures.
1 code implementation • 29 Sep 2023 • Yixing Huang, Christoph Bert, Ahmed Gomaa, Rainer Fietkau, Andreas Maier, Florian Putz
Incremental transfer learning, which combines peer-to-peer federated learning and domain incremental learning, can overcome the data privacy issue and meanwhile preserve model performance by using continual learning techniques.
no code implementations • 21 Sep 2023 • Zhisheng Wang, Zihan Deng, Fenglin Liu, Yixing Huang, Haijun Yu, Junning Cui
The second uses multiple networks to train different directional Hilbert filtering models for DBP images of multiple linear scannings, respectively, and then overlays the reconstructed results, i. e., Multiple Networks Overlaying (MNetO).
no code implementations • 26 Jun 2023 • Amr Hagag, Ahmed Gomaa, Dominik Kornek, Andreas Maier, Rainer Fietkau, Christoph Bert, Florian Putz, Yixing Huang
The StyleGAN2 is then used to embed the photographs to its highly expressive latent space.
no code implementations • 30 May 2023 • Zhisheng Wang, Haijun Yu, Yixing Huang, Shunli Wang, Song Ni, Zongfeng Li, Fenglin Liu, Junning Cui
Micro-computed tomography (micro-CT) is a widely used state-of-the-art instrument employed to study the morphological structures of objects in various fields.
1 code implementation • 24 Apr 2023 • Yixing Huang, Ahmed Gomaa, Sabine Semrau, Marlen Haderlein, Sebastian Lettmaier, Thomas Weissmann, Johanna Grigo, Hassen Ben Tkhayat, Benjamin Frey, Udo S. Gaipl, Luitpold V. Distel, Andreas Maier, Rainer Fietkau, Christoph Bert, Florian Putz
The potential of large language models in medicine for education and decision making purposes has been demonstrated as they achieve decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam.
no code implementations • 16 Apr 2023 • Florian Putz, Johanna Grigo, Thomas Weissmann, Philipp Schubert, Daniel Hoefler, Ahmed Gomaa, Hassen Ben Tkhayat, Amr Hagag, Sebastian Lettmaier, Benjamin Frey, Udo S. Gaipl, Luitpold V. Distel, Sabine Semrau, Christoph Bert, Rainer Fietkau, Yixing Huang
Conclusions: The Segment Anything foundation model, while trained on photos, can achieve high zero-shot accuracy for glioma brain tumor segmentation on MRI slices.
no code implementations • 1 Mar 2023 • Mareike Thies, Fabian Wagner, Mingxuan Gu, Siyuan Mei, Yixing Huang, Sabrina Pechmann, Oliver Aust, Daniela Weidner, Georgiana Neag, Stefan Uderhardt, Georg Schett, Silke Christiansen, Andreas Maier
Intravital X-ray microscopy (XRM) in preclinical mouse models is of vital importance for the identification of microscopic structural pathological changes in the bone which are characteristic of osteoporosis.
no code implementations • 17 Feb 2023 • Philipp Sommer, Yixing Huang, Christoph Bert, Andreas Maier, Manuel Schmidt, Arnd Dörfler, Rainer Fietkau, Florian Putz
We hypothesized that using radiomics and machine learning (ML), metastases at high risk for subsequent progression could be identified during follow-up prior to the onset of significant tumor growth, enabling personalized follow-up intervals and early selection for salvage treatment.
no code implementations • 29 Nov 2022 • Fuxin Fan, Yangkong Wang, Ludwig Ritschl, Ramyar Biniazan, Marcel Beister, Björn Kreher, Yixing Huang, Steffen Kappler, Andreas Maier
The existence of metallic implants in projection images for cone-beam computed tomography (CBCT) introduces undesired artifacts which degrade the quality of reconstructed images.
no code implementations • 28 Aug 2022 • Thomas Weissmann, Yixing Huang, Stefan Fischer, Johannes Roesch, Sina Mansoorian, Horacio Ayala Gaona, Antoniu-Oreste Gostian, Markus Hecht, Sebastian Lettmaier, Lisa Deloch, Benjamin Frey, Udo S. Gaipl, Luitpold V. Distel, Andreas Maier, Heinrich Iro, Sabine Semrau, Christoph Bert, Rainer Fietkau, Florian Putz
For a subgroup of 10 cases, intraobserver variability was compared to the average DL autosegmentation accuracy on the original and recontoured set of expert segmentations.
no code implementations • 15 Jul 2022 • Fabian Wagner, Mareike Thies, Felix Denzinger, Mingxuan Gu, Mayank Patwari, Stefan Ploner, Noah Maul, Laura Pfaff, Yixing Huang, Andreas Maier
Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality.
no code implementations • 26 Apr 2022 • Yixing Huang, Christoph Bert, Stefan Fischer, Manuel Schmidt, Arnd Dörfler, Andreas Maier, Rainer Fietkau, Florian Putz
With iterative continual learning (i. e., the shared model revisits each center multiple times during training), the sensitivity is further improved to 0. 914, which is identical to the sensitivity using mixed data for training.
no code implementations • 13 Feb 2022 • Yixing Huang, Andreas Maier, Fuxin Fan, Björn Kreher, Xiaolin Huang, Rainer Fietkau, Christoph Bert, Florian Putz
The complementary view setting provides a practical way to identify perspectively deformed structures by assessing the deviation between the two views.
1 code implementation • 25 Jan 2022 • Fabian Wagner, Mareike Thies, Mingxuan Gu, Yixing Huang, Sabrina Pechmann, Mayank Patwari, Stefan Ploner, Oliver Aust, Stefan Uderhardt, Georg Schett, Silke Christiansen, Andreas Maier
Due to the extremely low number of trainable parameters with well-defined effect, prediction reliance and data integrity is guaranteed at any time in the proposed pipelines, in contrast to most other deep learning-based denoising architectures.
1 code implementation • 22 Dec 2021 • Yixing Huang, Christoph Bert, Philipp Sommer, Benjamin Frey, Udo Gaipl, Luitpold V. Distel, Thomas Weissmann, Michael Uder, Manuel A. Schmidt, Arnd Dörfler, Andreas Maier, Rainer Fietkau, Florian Putz
To improve brain metastasis detection performance with deep learning, a custom detection loss called volume-level sensitivity-specificity (VSS) is proposed, which rates individual metastasis detection sensitivity and specificity in (sub-)volume levels.
no code implementations • 25 Aug 2021 • Fuxin Fan, Björn Kreher, Holger Keil, Andreas Maier, Yixing Huang
For direct detection from distorted markers in reconstructed volumes, an efficient automatic marker detection method using two neural networks and a conventional circle detection algorithm is proposed.
no code implementations • 7 Dec 2020 • Chang Liu, Yixing Huang, Joscha Maier, Laura Klein, Marc Kachelrieß, Andreas Maier
For organ-specific AEC, a preliminary CT reconstruction is necessary to estimate organ shapes for dose optimization, where only a few projections are allowed for real-time reconstruction.
no code implementations • 20 Nov 2020 • Leonid Mill, David Wolff, Nele Gerrits, Patrick Philipp, Lasse Kling, Florian Vollnhals, Andrew Ignatenko, Christian Jaremenko, Yixing Huang, Olivier De Castro, Jean-Nicolas Audinot, Inge Nelissen, Tom Wirtz, Andreas Maier, Silke Christiansen
Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health.
no code implementations • 9 Sep 2020 • Yixing Huang, Fuxin Fan, Christopher Syben, Philipp Roser, Leonid Mill, Andreas Maier
The method trained on conventional cephalograms can be directly applied to landmark detection in the synthetic cephalograms, achieving 93. 0% and 80. 7% successful detection rate in 4 mm precision range for synthetic cephalograms from 3D volumes and 2D projections respectively.
no code implementations • 18 Jun 2020 • Alexander Preuhs, Michael Manhart, Philipp Roser, Elisabeth Hoppe, Yixing Huang, Marios Psychogios, Markus Kowarschik, Andreas Maier
To this end, we train a siamese triplet network to predict the reprojection error (RPE) for the complete acquisition as well as an approximate distribution of the RPE along the single views from the reconstructed volume in a multi-task learning approach.
no code implementations • 20 May 2020 • Yixing Huang, Alexander Preuhs, Michael Manhart, Guenter Lauritsch, Andreas Maier
For example, for truncated data, DCR achieves a mean root-mean-square error of 24 HU and a mean structure similarity index of 0. 999 inside the field-of-view for different patients in the noisy case, while the state-of-the-art U-Net method achieves 55 HU and 0. 995 respectively for these two metrics.
no code implementations • 2 May 2020 • Lin Yuan, Yixing Huang, Andreas Maier
In this work, partial convolution is applied for projection inpainting, which only relies on valid pixels values.
no code implementations • 8 Jan 2020 • Yixing Huang, Shengxiang Wang, Yong Guan, Andreas Maier
Particularly, the U-Net, the state-of-the-art neural network in biomedical imaging, is trained from synthetic ellipsoid data and multi-category data to reduce artifacts in filtered back-projection (FBP) reconstruction images.
no code implementations • 4 Nov 2019 • Lei Gao, Yixing Huang, Andreas Maier
Background candidate images are obtained from input raw images with the masks.
no code implementations • 4 Nov 2019 • Yixing Huang, Lei Gao, Alexander Preuhs, Andreas Maier
In computed tomography (CT), data truncation is a common problem.
no code implementations • 19 Aug 2019 • Yixing Huang, Alexander Preuhs, Guenter Lauritsch, Michael Manhart, Xiaolin Huang, Andreas Maier
Robustness of deep learning methods for limited angle tomography is challenged by two major factors: a) due to insufficient training data the network may not generalize well to unseen data; b) deep learning methods are sensitive to noise.
no code implementations • 19 Dec 2017 • Yixing Huang, Oliver Taubmann, Xiaolin Huang, Viktor Haase, Guenter Lauritsch, Andreas Maier
Hence, the main purpose of this paper is to reduce streak artifacts at various scales.
no code implementations • 3 Jan 2017 • Xiaolin Huang, Yan Xia, Lei Shi, Yixing Huang, Ming Yan, Joachim Hornegger, Andreas Maier
Aiming at overexposure correction for computed tomography (CT) reconstruction, we in this paper propose a mixed one-bit compressive sensing (M1bit-CS) to acquire information from both regular and saturated measurements.