LIVIVO - Search results -

Search results

Result 1 - 10 of total 16

Search options

Book ; Conference proceedings: Medical Image Computing and Computer Assisted Intervention ¿ MICCAI 2023 Workshops

Woo, Jonghye / Mukherjee, Pritam / de Grauw, Max / Beets Tan, Regina / Corbetta, Valentina / Kotter, Elmar / Reyes, Mauricio / Baumgartner, Christian F. / Li, Quanzheng / Leahy, Richard / Dong, Bin / Presles, Benoît / Chen, Hao / Huo, Yuankai / Lv, Jinglei / Xu, Xinxing / Li, Xiaomeng / Mahapatra, Dwarikanath / Cheng, Li /

Petitjean, Caroline / Hering, Alessa / Silva, Wilson / Li, Xiang / Fu, Huazhu / Liu, Xiaofeng / Xing, Fangxu / Purushotham, Sanjay / Mathai, Tejas S.

MTSAIL 2023, LEAF 2023, AI4Treat 2023, MMMI 2023, REMIA 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8¿12, 2023, Proceedings

(Lecture Notes in Computer Science)

2024

Series title	Lecture Notes in Computer Science
Keywords	artificial intelligence ; Computer vision ; Machine Learning ; Medical Imaging ; explainability ; Privacy-Preserving Learning ; dermatology ; radiology ; motion tracking ; federated learning ; distributed learning ; Skin ; health informatics ; Radiomics ; video ; Time Series Data ; Physiological data ; Longitudinal Data ; Data Fusion ; Artificial Intelligence ; Computer Vision ; Explainability ; Federated Learning ; Distributed Learning ; Dermatology ; Radiology ; Health Informatics ; Video ; Physiological Data ; Motion Tracking
Language	English
Size	412 p.
Edition	1
Publisher	Springer International Publishing
Document type	Book ; Conference proceedings
Note	PDA Manuell_25
Format	155 x 235 x 23
ISBN	9783031474248 ; 3031474244
Database	PDA

Order for free: Patron-Driven-Acquisition (PDA)

This title is not yet in the ZB MED collection, but may be ordered for free via the ZB MED acquisition service PDA. The ordered item will be available in approximately one week.

Order via subito

This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Details ▾
- Order for free
- Order with fees

Book ; Online: Efficient Prealignment of CT Scans for Registration through a Bodypart Regressor

Meine, Hans / Hering, Alessa

2019

Abstract: Convolutional neural networks have not only been applied for classification of voxels, objects, or images, for instance, but have also been proposed as a bodypart regressor. We pick up this underexplored idea and evaluate its value for registration: A ... ...

Abstract	Convolutional neural networks have not only been applied for classification of voxels, objects, or images, for instance, but have also been proposed as a bodypart regressor. We pick up this underexplored idea and evaluate its value for registration: A CNN is trained to output the relative height within the human body in axial CT scans, and the resulting scores are used for quick alignment between different timepoints. Preliminary results confirm that this allows both fast and robust prealignment compared with iterative approaches. Comment: Extended Abstract accepted at MIDL 2019
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Publishing date	2019-09-19
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: CNN-based lung CT registration with multiple anatomical constraints.

Hering, Alessa / Häger, Stephanie / Moltz, Jan / Lessmann, Nikolas / Heldmann, Stefan / van Ginneken, Bram

Medical image analysis

2021 Volume 72, Page(s) 102139

Abstract: Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods. However, these methods often still cannot achieve the same performance as conventional registration methods because they are either limited to ... ...

Abstract	Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods. However, these methods often still cannot achieve the same performance as conventional registration methods because they are either limited to small deformation or they fail to handle a superposition of large and small deformations without producing implausible deformation fields with foldings inside. In this paper, we identify important strategies of conventional registration methods for lung registration and successfully developed the deep-learning counterpart. We employ a Gaussian-pyramid-based multilevel framework that can solve the image registration optimization in a coarse-to-fine fashion. Furthermore, we prevent foldings of the deformation field and restrict the determinant of the Jacobian to physiologically meaningful values by combining a volume change penalty with a curvature regularizer in the loss function. Keypoint correspondences are integrated to focus on the alignment of smaller structures. We perform an extensive evaluation to assess the accuracy, the robustness, the plausibility of the estimated deformation fields, and the transferability of our registration approach. We show that it achieves state-of-the-art results on the COPDGene dataset compared to conventional registration method with much shorter execution time. In our experiments on the DIRLab exhale to inhale lung registration, we demonstrate substantial improvements (TRE below 1.2 mm) over other deep learning methods. Our algorithm is publicly available at https://grand-challenge.org/algorithms/deep-learning-based-ct-lung-registration/.
MeSH term(s)	Algorithms ; Humans ; Image Processing, Computer-Assisted ; Lung/diagnostic imaging ; Thorax ; Tomography, X-Ray Computed
Language	English
Publishing date	2021-06-22
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1356436-5
ISSN	1361-8423 ; 1361-8431 ; 1361-8415
ISSN (online)	1361-8423 ; 1361-8431
ISSN	1361-8415
DOI	10.1016/j.media.2021.102139
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 5206: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: Memory-efficient 2.5D convolutional transformer networks for multi-modal deformable registration with weak label supervision applied to whole-heart CT and MRI scans.

Hering, Alessa / Kuckertz, Sven / Heldmann, Stefan / Heinrich, Mattias P

International journal of computer assisted radiology and surgery

2019 Volume 14, Issue 11, Page(s) 1901–1912

Abstract: PURPOSE : Despite its potential for improvements through supervision, deep learning-based registration approaches are difficult to train for large deformations in 3D scans due to excessive memory requirements. METHODS : We propose a new 2.5D ... ...

Abstract	PURPOSE : Despite its potential for improvements through supervision, deep learning-based registration approaches are difficult to train for large deformations in 3D scans due to excessive memory requirements. METHODS : We propose a new 2.5D convolutional transformer architecture that enables us to learn a memory-efficient weakly supervised deep learning model for multi-modal image registration. Furthermore, we firstly integrate a volume change control term into the loss function of a deep learning-based registration method to penalize occurring foldings inside the deformation field. RESULTS : Our approach succeeds at learning large deformations across multi-modal images. We evaluate our approach on 100 pair-wise registrations of CT and MRI whole-heart scans and demonstrate considerably higher Dice Scores (of 0.74) compared to a state-of-the-art unsupervised discrete registration framework (deeds with Dice of 0.71). CONCLUSION : Our proposed memory-efficient registration method performs better than state-of-the-art conventional registration methods. By using a volume change control term in the loss function, the number of occurring foldings can be considerably reduced on new registration cases.
MeSH term(s)	Deep Learning ; Equipment Design ; Heart/diagnostic imaging ; Humans ; Magnetic Resonance Imaging/instrumentation ; Neural Networks, Computer ; Phantoms, Imaging ; Tomography, X-Ray Computed/instrumentation
Language	English
Publishing date	2019-09-19
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2365628-1
ISSN	1861-6429 ; 1861-6410
ISSN (online)	1861-6429
ISSN	1861-6410
DOI	10.1007/s11548-019-02068-z
Database	MEDical Literature Analysis and Retrieval System OnLINE

Full text online

Accessible to users with ZB MED library card

In stock of ZB MED Cologne/Königswinter

Zs.A 6615: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾

Article: Can Whole-Body Baseline CT Radiomics Add Information to the Prediction of Best Response, Progression-Free Survival, and Overall Survival of Stage IV Melanoma Patients Receiving First-Line Targeted Therapy: A Retrospective Register Study.

Peisen, Felix / Gerken, Annika / Hering, Alessa / Dahm, Isabel / Nikolaou, Konstantin / Gatidis, Sergios / Eigentler, Thomas K / Amaral, Teresa / Moltz, Jan H / Othman, Ahmed E

Diagnostics (Basel, Switzerland)

2023 Volume 13, Issue 20

Abstract: Background: The aim of this study was to investigate whether the combination of radiomics and clinical parameters in a machine-learning model offers additive information compared with the use of only clinical parameters in predicting the best response, ... ...

Abstract	Background: The aim of this study was to investigate whether the combination of radiomics and clinical parameters in a machine-learning model offers additive information compared with the use of only clinical parameters in predicting the best response, progression-free survival after six months, as well as overall survival after six and twelve months in patients with stage IV malignant melanoma undergoing first-line targeted therapy. Methods: A baseline machine-learning model using clinical variables (demographic parameters and tumor markers) was compared with an extended model using clinical variables and radiomic features of the whole tumor burden, utilizing repeated five-fold cross-validation. Baseline CTs of 91 stage IV malignant melanoma patients, all treated in the same university hospital, were identified in the Central Malignant Melanoma Registry and all metastases were volumetrically segmented ( Results: Compared with the baseline model, the extended radiomics model did not add significantly more information to the best-response prediction (AUC [95% CI] 0.548 (0.188, 0.808) vs. 0.487 (0.139, 0.743)), the prediction of PFS after six months (AUC [95% CI] 0.699 (0.436, 0.958) vs. 0.604 (0.373, 0.867)), or the overall survival prediction after six and twelve months (AUC [95% CI] 0.685 (0.188, 0.967) vs. 0.766 (0.433, 1.000) and AUC [95% CI] 0.554 (0.163, 0.781) vs. 0.616 (0.271, 1.000), respectively). Conclusions: The results showed no additional value of baseline whole-body CT radiomics for best-response prediction, progression-free survival prediction for six months, or six-month and twelve-month overall survival prediction for stage IV melanoma patients receiving first-line targeted therapy. These results need to be validated in a larger cohort.
Language	English
Publishing date	2023-10-14
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics13203210
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Book ; Online: mlVIRNET

Hering, Alessa / van Ginneken, Bram / Heldmann, Stefan

Multilevel Variational Image Registration Network

2019

Abstract: We present a novel multilevel approach for deep learning based image registration. Recently published deep learning based registration methods have shown promising results for a wide range of tasks. However, these algorithms are still limited to ... ...

Abstract	We present a novel multilevel approach for deep learning based image registration. Recently published deep learning based registration methods have shown promising results for a wide range of tasks. However, these algorithms are still limited to relatively small deformations. Our method addresses this shortcoming by introducing a multilevel framework, which computes deformation fields on different scales, similar to conventional methods. Thereby, a coarse-level alignment is obtained first, which is subsequently improved on finer levels. We demonstrate our method on the complex task of inhale-to-exhale lung registration. We show that the use of a deep learning multilevel approach leads to significantly better registration results. Comment: accepted for publication at MICCAI 2019
Keywords	Computer Science - Computer Vision and Pattern Recognition
Publishing date	2019-09-22
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Book ; Online: Automatic segmentation of the pulmonary lobes with a 3D u-net and optimized loss function

Lassen-Schmidt, Bianca / Hering, Alessa / Krass, Stefan / Meine, Hans

2020

Abstract: Fully-automatic lung lobe segmentation is challenging due to anatomical variations, pathologies, and incomplete fissures. We trained a 3D u-net for pulmonary lobe segmentation on 49 mainly publically available datasets and introduced a weighted Dice loss ...

Abstract	Fully-automatic lung lobe segmentation is challenging due to anatomical variations, pathologies, and incomplete fissures. We trained a 3D u-net for pulmonary lobe segmentation on 49 mainly publically available datasets and introduced a weighted Dice loss function to emphasize the lobar boundaries. To validate the performance of the proposed method we compared the results to two other methods. The new loss function improved the mean distance to 1.46 mm (compared to 2.08 mm for simple loss function without weighting). Comment: MIDL2020 short paper
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Publishing date	2020-05-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Quantitative evaluation of the influence of multiple MRI sequences and of pathological tissues on the registration of longitudinal data acquired during brain tumor treatment.

Canalini, Luca / Klein, Jan / Waldmannstetter, Diana / Kofler, Florian / Cerri, Stefano / Hering, Alessa / Heldmann, Stefan / Schlaeger, Sarah / Menze, Bjoern H / Wiestler, Benedikt / Kirschke, Jan / Hahn, Horst K

Frontiers in neuroimaging

2022 Volume 1, Page(s) 977491

Abstract: Registration methods facilitate the comparison of multiparametric magnetic resonance images acquired at different stages of brain tumor treatments. Image-based registration solutions are influenced by the sequences chosen to compute the distance measure, ...

Abstract	Registration methods facilitate the comparison of multiparametric magnetic resonance images acquired at different stages of brain tumor treatments. Image-based registration solutions are influenced by the sequences chosen to compute the distance measure, and the lack of image correspondences due to the resection cavities and pathological tissues. Nonetheless, an evaluation of the impact of these input parameters on the registration of longitudinal data is still missing. This work evaluates the influence of multiple sequences, namely T1-weighted (T1), T2-weighted (T2), contrast enhanced T1-weighted (T1-CE), and T2 Fluid Attenuated Inversion Recovery (FLAIR), and the exclusion of the pathological tissues on the non-rigid registration of pre- and post-operative images. We here investigate two types of registration methods, an iterative approach and a convolutional neural network solution based on a 3D U-Net. We employ two test sets to compute the mean target registration error (mTRE) based on corresponding landmarks. In the first set, markers are positioned exclusively in the surroundings of the pathology. The methods employing T1-CE achieves the lowest mTREs, with a improvement up to 0.8 mm for the iterative solution. The results are higher than the baseline when using the FLAIR sequence. When excluding the pathology, lower mTREs are observable for most of the methods. In the second test set, corresponding landmarks are located in the entire brain volumes. Both solutions employing T1-CE obtain the lowest mTREs, with a decrease up to 1.16 mm for the iterative method, whereas the results worsen using the FLAIR. When excluding the pathology, an improvement is observable for the CNN method using T1-CE. Both approaches utilizing the T1-CE sequence obtain the best mTREs, whereas the FLAIR is the least informative to guide the registration process. Besides, the exclusion of pathology from the distance measure computation improves the registration of the brain tissues surrounding the tumor. Thus, this work provides the first numerical evaluation of the influence of these parameters on the registration of longitudinal magnetic resonance images, and it can be helpful for developing future algorithms.
Language	English
Publishing date	2022-09-20
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	3123824-5
ISSN	2813-1193 ; 2813-1193
ISSN (online)	2813-1193
ISSN	2813-1193
DOI	10.3389/fnimg.2022.977491
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article: Combination of Whole-Body Baseline CT Radiomics and Clinical Parameters to Predict Response and Survival in a Stage-IV Melanoma Cohort Undergoing Immunotherapy.

Peisen, Felix / Hänsch, Annika / Hering, Alessa / Brendlin, Andreas S / Afat, Saif / Nikolaou, Konstantin / Gatidis, Sergios / Eigentler, Thomas / Amaral, Teresa / Moltz, Jan H / Othman, Ahmed E

Cancers

2022 Volume 14, Issue 12

Abstract: Background: This study investigated whether a machine-learning-based combination of radiomics and clinical parameters was superior to the use of clinical parameters alone in predicting therapy response after three months, and overall survival after six ... ...

Abstract	Background: This study investigated whether a machine-learning-based combination of radiomics and clinical parameters was superior to the use of clinical parameters alone in predicting therapy response after three months, and overall survival after six and twelve months, in stage-IV malignant melanoma patients undergoing immunotherapy with PD-1 checkpoint inhibitors and CTLA-4 checkpoint inhibitors. Methods: A random forest model using clinical parameters (demographic variables and tumor markers = baseline model) was compared to a random forest model using clinical parameters and radiomics (extended model) via repeated 5-fold cross-validation. For this purpose, the baseline computed tomographies of 262 stage-IV malignant melanoma patients treated at a tertiary referral center were identified in the Central Malignant Melanoma Registry, and all visible metastases were three-dimensionally segmented ( Results: The extended model was not significantly superior compared to the baseline model for survival prediction after six and twelve months (AUC (95% CI): 0.664 (0.598, 0.729) vs. 0.620 (0.545, 0.692) and AUC (95% CI): 0.600 (0.526, 0.667) vs. 0.588 (0.481, 0.629), respectively). The extended model was not significantly superior compared to the baseline model for response prediction after three months (AUC (95% CI): 0.641 (0.581, 0.700) vs. 0.656 (0.587, 0.719)). Conclusions: The study indicated a potential, but non-significant, added value of radiomics for six-month and twelve-month survival prediction of stage-IV melanoma patients undergoing immunotherapy.
Language	English
Publishing date	2022-06-17
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers14122992
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Book ; Online: CNN-based Lung CT Registration with Multiple Anatomical Constraints

Hering, Alessa / Häger, Stephanie / Moltz, Jan / Lessmann, Nikolas / Heldmann, Stefan / van Ginneken, Bram

2020

Abstract	Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods. However, these methods often still cannot achieve the same performance as conventional registration methods because they are either limited to small deformation or they fail to handle a superposition of large and small deformations without producing implausible deformation fields with foldings inside. In this paper, we identify important strategies of conventional registration methods for lung registration and successfully developed the deep-learning counterpart. We employ a Gaussian-pyramid-based multilevel framework that can solve the image registration optimization in a coarse-to-fine fashion. Furthermore, we prevent foldings of the deformation field and restrict the determinant of the Jacobian to physiologically meaningful values by combining a volume change penalty with a curvature regularizer in the loss function. Keypoint correspondences are integrated to focus on the alignment of smaller structures. We perform an extensive evaluation to assess the accuracy, the robustness, the plausibility of the estimated deformation fields, and the transferability of our registration approach. We show that it achieves state-of-the-art results on the COPDGene dataset compared to conventional registration method with much shorter execution time. In our experiments on the DIRLab exhale to inhale lung registration, we demonstrate substantial improvements (TRE below $1.2$ mm) over other deep learning methods. Our algorithm is publicly available at https://grand-challenge.org/algorithms/deep-learning-based-ct-lung-registration/.
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2020-11-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

To top

Kategorien

Order for free: Patron-Driven-Acquisition (PDA)

Order via subito

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

Full text online

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

Order via subito

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

Full text online

More links

Kategorien

Inter-library loan at ZB MED