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  1. Book ; Online: ValUES

    Kahl, Kim-Celine / Lüth, Carsten T. / Zenk, Maximilian / Maier-Hein, Klaus / Jaeger, Paul F.

    A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation

    2024  

    Abstract: Uncertainty estimation is an essential and heavily-studied component for the reliable application of semantic segmentation methods. While various studies exist claiming methodological advances on the one hand, and successful application on the other hand, ...

    Abstract Uncertainty estimation is an essential and heavily-studied component for the reliable application of semantic segmentation methods. While various studies exist claiming methodological advances on the one hand, and successful application on the other hand, the field is currently hampered by a gap between theory and practice leaving fundamental questions unanswered: Can data-related and model-related uncertainty really be separated in practice? Which components of an uncertainty method are essential for real-world performance? Which uncertainty method works well for which application? In this work, we link this research gap to a lack of systematic and comprehensive evaluation of uncertainty methods. Specifically, we identify three key pitfalls in current literature and present an evaluation framework that bridges the research gap by providing 1) a controlled environment for studying data ambiguities as well as distribution shifts, 2) systematic ablations of relevant method components, and 3) test-beds for the five predominant uncertainty applications: OoD-detection, active learning, failure detection, calibration, and ambiguity modeling. Empirical results on simulated as well as real-world data demonstrate how the proposed framework is able to answer the predominant questions in the field revealing for instance that 1) separation of uncertainty types works on simulated data but does not necessarily translate to real-world data, 2) aggregation of scores is a crucial but currently neglected component of uncertainty methods, 3) While ensembles are performing most robustly across the different downstream tasks and settings, test-time augmentation often constitutes a light-weight alternative. Code is at: https://github.com/IML-DKFZ/values

    Comment: ICLR 2024 (oral)
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 004
    Publishing date 2024-01-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Deep Learning for Automatic Bone Marrow Apparent Diffusion Coefficient Measurements From Whole-Body Magnetic Resonance Imaging in Patients With Multiple Myeloma: A Retrospective Multicenter Study.

    Wennmann, Markus / Neher, Peter / Stanczyk, Nikolas / Kahl, Kim-Celine / Kächele, Jessica / Weru, Vivienn / Hielscher, Thomas / Grözinger, Martin / Chmelik, Jiri / Zhang, Kevin Sun / Bauer, Fabian / Nonnenmacher, Tobias / Debic, Manuel / Sauer, Sandra / Rotkopf, Lukas Thomas / Jauch, Anna / Schlamp, Kai / Mai, Elias Karl / Weinhold, Niels /
    Afat, Saif / Horger, Marius / Goldschmidt, Hartmut / Schlemmer, Heinz-Peter / Weber, Tim Frederik / Delorme, Stefan / Kurz, Felix Tobias / Maier-Hein, Klaus

    Investigative radiology

    2022  Volume 58, Issue 4, Page(s) 273–282

    Abstract: Objectives: Diffusion-weighted magnetic resonance imaging (MRI) is increasingly important in patients with multiple myeloma (MM). The objective of this study was to train and test an algorithm for automatic pelvic bone marrow analysis from whole-body ... ...

    Abstract Objectives: Diffusion-weighted magnetic resonance imaging (MRI) is increasingly important in patients with multiple myeloma (MM). The objective of this study was to train and test an algorithm for automatic pelvic bone marrow analysis from whole-body apparent diffusion coefficient (ADC) maps in patients with MM, which automatically segments pelvic bones and subsequently extracts objective, representative ADC measurements from each bone.
    Materials and methods: In this retrospective multicentric study, 180 MRIs from 54 patients were annotated (semi)manually and used to train an nnU-Net for automatic, individual segmentation of the right hip bone, the left hip bone, and the sacral bone. The quality of the automatic segmentation was evaluated on 15 manually segmented whole-body MRIs from 3 centers using the dice score. In 3 independent test sets from 3 centers, which comprised a total of 312 whole-body MRIs, agreement between automatically extracted mean ADC values from the nnU-Net segmentation and manual ADC measurements from 2 independent radiologists was evaluated. Bland-Altman plots were constructed, and absolute bias, relative bias to mean, limits of agreement, and coefficients of variation were calculated. In 56 patients with newly diagnosed MM who had undergone bone marrow biopsy, ADC measurements were correlated with biopsy results using Spearman correlation.
    Results: The ADC-nnU-Net achieved automatic segmentations with mean dice scores of 0.92, 0.93, and 0.85 for the right pelvis, the left pelvis, and the sacral bone, whereas the interrater experiment gave mean dice scores of 0.86, 0.86, and 0.77, respectively. The agreement between radiologists' manual ADC measurements and automatic ADC measurements was as follows: the bias between the first reader and the automatic approach was 49 × 10 -6 mm 2 /s, 7 × 10 -6 mm 2 /s, and -58 × 10 -6 mm 2 /s, and the bias between the second reader and the automatic approach was 12 × 10 -6 mm 2 /s, 2 × 10 -6 mm 2 /s, and -66 × 10 -6 mm 2 /s for the right pelvis, the left pelvis, and the sacral bone, respectively. The bias between reader 1 and reader 2 was 40 × 10 -6 mm 2 /s, 8 × 10 -6 mm 2 /s, and 7 × 10 -6 mm 2 /s, and the mean absolute difference between manual readers was 84 × 10 -6 mm 2 /s, 65 × 10 -6 mm 2 /s, and 75 × 10 -6 mm 2 /s. Automatically extracted ADC values significantly correlated with bone marrow plasma cell infiltration ( R = 0.36, P = 0.007).
    Conclusions: In this study, a nnU-Net was trained that can automatically segment pelvic bone marrow from whole-body ADC maps in multicentric data sets with a quality comparable to manual segmentations. This approach allows automatic, objective bone marrow ADC measurements, which agree well with manual ADC measurements and can help to overcome interrater variability or nonrepresentative measurements. Automatically extracted ADC values significantly correlate with bone marrow plasma cell infiltration and might be of value for automatic staging, risk stratification, or therapy response assessment.
    MeSH term(s) Humans ; Magnetic Resonance Imaging/methods ; Multiple Myeloma/diagnostic imaging ; Multiple Myeloma/pathology ; Bone Marrow/diagnostic imaging ; Deep Learning ; Retrospective Studies ; Whole Body Imaging/methods ; Diffusion Magnetic Resonance Imaging/methods
    Language English
    Publishing date 2022-10-03
    Publishing country United States
    Document type Multicenter Study ; Journal Article
    ZDB-ID 80345-5
    ISSN 1536-0210 ; 0020-9996
    ISSN (online) 1536-0210
    ISSN 0020-9996
    DOI 10.1097/RLI.0000000000000932
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics.

    Wennmann, Markus / Ming, Wenlong / Bauer, Fabian / Chmelik, Jiri / Klein, André / Uhlenbrock, Charlotte / Grözinger, Martin / Kahl, Kim-Celine / Nonnenmacher, Tobias / Debic, Manuel / Hielscher, Thomas / Thierjung, Heidi / Rotkopf, Lukas T / Stanczyk, Nikolas / Sauer, Sandra / Jauch, Anna / Götz, Michael / Kurz, Felix T / Schlamp, Kai /
    Horger, Marius / Afat, Saif / Besemer, Britta / Hoffmann, Martin / Hoffend, Johannes / Kraemer, Doris / Graeven, Ullrich / Ringelstein, Adrian / Bonekamp, David / Kleesiek, Jens / Floca, Ralf O / Hillengass, Jens / Mai, Elias K / Weinhold, Niels / Weber, Tim F / Goldschmidt, Hartmut / Schlemmer, Heinz-Peter / Maier-Hein, Klaus / Delorme, Stefan / Neher, Peter

    Investigative radiology

    2023  Volume 58, Issue 10, Page(s) 754–765

    Abstract: Objectives: In multiple myeloma and its precursor stages, plasma cell infiltration (PCI) and cytogenetic aberrations are important for staging, risk stratification, and response assessment. However, invasive bone marrow (BM) biopsies cannot be performed ...

    Abstract Objectives: In multiple myeloma and its precursor stages, plasma cell infiltration (PCI) and cytogenetic aberrations are important for staging, risk stratification, and response assessment. However, invasive bone marrow (BM) biopsies cannot be performed frequently and multifocally to assess the spatially heterogenous tumor tissue. Therefore, the goal of this study was to establish an automated framework to predict local BM biopsy results from magnetic resonance imaging (MRI).
    Materials and methods: This retrospective multicentric study used data from center 1 for algorithm training and internal testing, and data from center 2 to 8 for external testing. An nnU-Net was trained for automated segmentation of pelvic BM from T1-weighted whole-body MRI. Radiomics features were extracted from these segmentations, and random forest models were trained to predict PCI and the presence or absence of cytogenetic aberrations. Pearson correlation coefficient and the area under the receiver operating characteristic were used to evaluate the prediction performance for PCI and cytogenetic aberrations, respectively.
    Results: A total of 672 MRIs from 512 patients (median age, 61 years; interquartile range, 53-67 years; 307 men) from 8 centers and 370 corresponding BM biopsies were included. The predicted PCI from the best model was significantly correlated ( P ≤ 0.01) to the actual PCI from biopsy in all internal and external test sets (internal test set: r = 0.71 [0.51, 0.83]; center 2, high-quality test set: r = 0.45 [0.12, 0.69]; center 2, other test set: r = 0.30 [0.07, 0.49]; multicenter test set: r = 0.57 [0.30, 0.76]). The areas under the receiver operating characteristic of the prediction models for the different cytogenetic aberrations ranged from 0.57 to 0.76 for the internal test set, but no model generalized well to all 3 external test sets.
    Conclusions: The automated image analysis framework established in this study allows for noninvasive prediction of a surrogate parameter for PCI, which is significantly correlated to the actual PCI from BM biopsy.
    MeSH term(s) Male ; Humans ; Middle Aged ; Multiple Myeloma/diagnostic imaging ; Multiple Myeloma/genetics ; Bone Marrow/diagnostic imaging ; Deep Learning ; Retrospective Studies ; Magnetic Resonance Imaging/methods ; Biopsy ; Chromosome Aberrations
    Language English
    Publishing date 2023-05-22
    Publishing country United States
    Document type Multicenter Study ; Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 80345-5
    ISSN 1536-0210 ; 0020-9996
    ISSN (online) 1536-0210
    ISSN 0020-9996
    DOI 10.1097/RLI.0000000000000986
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 734: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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