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  1. Book ; Conference proceedings: Medical computer vision

    Menze, Bjoern Holger

    recognition techniques and applications in medical imaging ; second International MICCAI Workshop, MCV 2012, Nice, France, October 5, 2012, revised selected papers

    (Lecture notes in computer science ; 7766)

    2013  

    Event/congress MCV (2, 2012, Nizza)
    Author's details Bjoern H. Menze ... (ed.)
    Series title Lecture notes in computer science ; 7766
    Collection
    Keywords Bildgebendes Verfahren ; Bildverarbeitung
    Subject Automatische Bildverarbeitung ; Bilddatenverarbeitung ; Elektronische Bildverarbeitung ; Imageprocessing ; Image processing ; Imaging ; Digitale Bildverarbeitung ; Picture processing ; Bildbearbeitung ; Bildgebendes Diagnoseverfahren ; Diagnostik ; Bilddiagnostik ; Bildgebende Methode ; Medical Imaging ; Medizinische Bildgebung ; Bildgebende Diagnostik ; Bildgebende Verfahren
    Language English
    Size XI, 294 S. : Ill., graph. Darst., 235 mm x 155 mm
    Publisher Springer
    Publishing place Heidelberg u.a.
    Publishing country Germany
    Document type Book ; Conference proceedings
    HBZ-ID HT017631344
    ISBN 978-3-642-36619-2 ; 3-642-36619-8 ; 9783642366208 ; 3642366201
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    2013 A 771 available for loan (reading room) Lesesaal EG

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  2. Book ; Thesis: Mustererkennung in der quantitativen Auswertung vektor-wertiger Bilddaten

    Menze, Bjoern Holger

    diagnostische Systeme und Anwendungen = Pattern recognition in the quantitative analysis of vector valued image data

    2007  

    Title variant Pattern recognition in the quantitative analysis of vector valued image data ; Pattern recognition in the quantitative analysis of vector-valued image data
    Author's details [von Bjoern Holger Menze]
    Language English
    Size 292 Bl. : Ill., graph. Darst.
    Edition [Mikrofiche-Ausg.]
    Publishing country Germany
    Document type Book ; Thesis
    Thesis / German Habilitation thesis Heidelberg, Univ., Diss., 2007
    Note Zsfassung in dt. Sprache
    HBZ-ID HT015350795
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    2004 MB 415 order for loan Magazin

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  3. Book ; Online ; Thesis: Shortwave-infrared Line-Scanning Confocal Microscope for Deep Tissue Imaging

    Lingg, Jakob Gerhard Peter Verfasser] / [Menze, Bjoern Holger [Akademischer Betreuer] / Bruns, Oliver Gutachter] / [Menze, Bjoern Holger [Gutachter]

    2024  

    Author's details Jakob Gerhard Peter Lingg ; Gutachter: Oliver Bruns, Bjoern Menze ; Betreuer: Bjoern Menze
    Keywords Naturwissenschaften ; Science
    Subject code sg500
    Language English
    Publisher Universitätsbibliothek der TU München
    Publishing place München
    Document type Book ; Online ; Thesis
    Database Digital theses on the web

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  4. Book ; Online ; Thesis: Machine Learning Characterization of Vascular Functions in Stroke Perfusion Imaging

    de la Rosa, Ezequiel Verfasser] / [Menze, Bjoern Holger [Akademischer Betreuer] / Vandemeulebroucke, Jef Gutachter] / [Menze, Bjoern Holger [Gutachter]

    2024  

    Author's details Ezequiel de la Rosa ; Gutachter: Jef Vandemeulebroucke, Björn H. Menze ; Betreuer: Björn H. Menze
    Keywords Medizin, Gesundheit ; Medicine, Health
    Subject code sg610
    Language English
    Publisher Universitätsbibliothek der TU München
    Publishing place München
    Document type Book ; Online ; Thesis
    Database Digital theses on the web

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  5. Article: Leveraging 2D Deep Learning ImageNet-trained models for Native 3D Medical Image Analysis.

    Baheti, Bhakti / Pati, Sarthak / Menze, Bjoern / Bakas, Spyridon

    Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop)

    2023  Volume 13769, Page(s) 68–79

    Abstract: Convolutional neural networks (CNNs) have shown promising performance in various 2D computer vision tasks due to availability of large amounts of 2D training data. Contrarily, medical imaging deals with 3D data and usually lacks the equivalent extent and ...

    Abstract Convolutional neural networks (CNNs) have shown promising performance in various 2D computer vision tasks due to availability of large amounts of 2D training data. Contrarily, medical imaging deals with 3D data and usually lacks the equivalent extent and diversity of data, for developing AI models. Transfer learning provides the means to use models trained for one application as a starting point to another application. In this work, we leverage 2D pre-trained models as a starting point in 3D medical applications by exploring the concept of Axial-Coronal-Sagittal (ACS) convolutions. We have incorporated ACS as an alternative of native 3D convolutions in the Generally Nuanced Deep Learning Framework (GaNDLF), providing various well-established and state-of-the-art network architectures with the availability of pre-trained encoders from 2D data. Results of our experimental evaluation on 3D MRI data of brain tumor patients for i) tumor segmentation and ii) radiogenomic classification, show model size reduction by ~22% and improvement in validation accuracy by ~33%. Our findings support the advantage of ACS convolutions in pre-trained 2D CNNs over 3D CNN without pre-training, for 3D segmentation and classification tasks, democratizing existing models trained in datasets of unprecedented size and showing promise in the field of healthcare.
    Language English
    Publishing date 2023-07-18
    Publishing country Switzerland
    Document type Journal Article
    DOI 10.1007/978-3-031-33842-7_6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Book ; Online ; Conference proceedings: "Medical Computer Vision: Algorithms for Big Data"

    Menze, Bjoern

    International Workshop, MCV 2015, held in conjunction with MICCAI 2015, Munich, Germany, October 9, 2015 : revised selected papers

    (Lecture notes in computer science, ; 9601 ; LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics)

    2016  

    Title variant MCV Workshop 2015
    Event/congress MCV (Workshop) (5th, 2015, MunichGermany) ; International Conference on Medical Image Computing and Computer-Assisted Intervention (18th, 2015, MunichGermany)
    Author's details Bjoern Menze [and 7 others] (eds.)
    Series title Lecture notes in computer science, ; 9601 ; LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics
    MeSH term(s) Image Processing, Computer-Assisted/methods ; Diagnostic Imaging/methods
    Language English
    Size xv, 182 pages :, illustrations.
    Document type Book ; Online ; Conference proceedings
    ISBN 9783319420158 ; 9783319420165 ; 3319420151 ; 331942016X
    DOI 10.1007/978-3-319-42016-5
    Database Catalogue of the US National Library of Medicine (NLM)

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  7. Article ; Online: Deep learning for medical image analysis: a brief introduction.

    Wiestler, Benedikt / Menze, Bjoern

    Neuro-oncology advances

    2021  Volume 2, Issue Suppl 4, Page(s) iv35–iv41

    Abstract: Advances in deep learning have led to the development of neural network algorithms which today rival human performance in vision tasks, such as image classification or segmentation. Translation of these techniques into clinical science has also ... ...

    Abstract Advances in deep learning have led to the development of neural network algorithms which today rival human performance in vision tasks, such as image classification or segmentation. Translation of these techniques into clinical science has also significantly advanced image analysis in neuro-oncology. This has created a need in the neuro-oncology community for understanding the mechanisms behind neural networks and deep learning, as close interaction of computer scientists and neuro-oncology researchers as well as realistic expectations about the possibilities (and limitations) of the current state-of-the-art is pivotal for successful translation of deep learning techniques into practice. In this review, we will briefly introduce the building blocks of neural networks with a particular focus on convolutional neural networks. We will explain why these networks excel at identifying relevant features and how they learn to associate these imaging features with (clinical) features of interest, such as genotype, or how they automatically segment structures of interest in the image volume. We will also discuss challenges for the more widespread use of these algorithms.
    Language English
    Publishing date 2021-01-23
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 3009682-0
    ISSN 2632-2498 ; 2632-2498
    ISSN (online) 2632-2498
    ISSN 2632-2498
    DOI 10.1093/noajnl/vdaa092
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Learning continuous shape priors from sparse data with neural implicit functions.

    Amiranashvili, Tamaz / Lüdke, David / Li, Hongwei Bran / Zachow, Stefan / Menze, Bjoern H

    Medical image analysis

    2024  Volume 94, Page(s) 103099

    Abstract: Statistical shape models are an essential tool for various tasks in medical image analysis, including shape generation, reconstruction and classification. Shape models are learned from a population of example shapes, which are typically obtained through ... ...

    Abstract Statistical shape models are an essential tool for various tasks in medical image analysis, including shape generation, reconstruction and classification. Shape models are learned from a population of example shapes, which are typically obtained through segmentation of volumetric medical images. In clinical practice, highly anisotropic volumetric scans with large slice distances are prevalent, e.g., to reduce radiation exposure in CT or image acquisition time in MR imaging. For existing shape modeling approaches, the resolution of the emerging model is limited to the resolution of the training shapes. Therefore, any missing information between slices prohibits existing methods from learning a high-resolution shape prior. We propose a novel shape modeling approach that can be trained on sparse, binary segmentation masks with large slice distances. This is achieved through employing continuous shape representations based on neural implicit functions. After training, our model can reconstruct shapes from various sparse inputs at high target resolutions beyond the resolution of individual training examples. We successfully reconstruct high-resolution shapes from as few as three orthogonal slices. Furthermore, our shape model allows us to embed various sparse segmentation masks into a common, low-dimensional latent space - independent of the acquisition direction, resolution, spacing, and field of view. We show that the emerging latent representation discriminates between healthy and pathological shapes, even when provided with sparse segmentation masks. Lastly, we qualitatively demonstrate that the emerging latent space is smooth and captures characteristic modes of shape variation. We evaluate our shape model on two anatomical structures: the lumbar vertebra and the distal femur, both from publicly available datasets.
    MeSH term(s) Humans ; Algorithms ; Models, Statistical ; Magnetic Resonance Imaging ; Image Processing, Computer-Assisted/methods
    Language English
    Publishing date 2024-02-08
    Publishing country Netherlands
    Document type Journal Article
    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.2024.103099
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    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)

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  9. Book ; Online ; Thesis: Predictive modelling of cancer chromosomal instability

    Zhang, Xiaoxiao Verfasser] / [Menze, Bjoern Holger [Akademischer Betreuer] / Menze, Bjoern Holger [Gutachter] / Bastians, Holger [Gutachter]

    2023  

    Author's details Xiaoxiao Zhang ; Gutachter: Björn Menze, Holger Bastians ; Betreuer: Björn Menze
    Keywords Medizin, Gesundheit ; Medicine, Health
    Subject code sg610
    Language English
    Publisher Universitätsbibliothek der TU München
    Publishing place München
    Document type Book ; Online ; Thesis
    Database Digital theses on the web

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  10. Book ; Online ; Thesis: The Hitchhiker's Guide to Machine Learning for Biomedical Image Analysis

    Kofler, Florian Verfasser] / [Menze, Bjoern Holger [Akademischer Betreuer] / Menze, Bjoern Holger [Gutachter] / Wiest, Roland [Gutachter]

    2023  

    Author's details Florian Kofler ; Gutachter: Björn Menze, Roland Wiest ; Betreuer: Björn Menze
    Keywords Naturwissenschaften ; Science
    Subject code sg500
    Language English
    Publisher Universitätsbibliothek der TU München
    Publishing place München
    Document type Book ; Online ; Thesis
    Database Digital theses on the web

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