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  • Springer Science and Business Media LLC  (15)
  • 1
    Online Resource
    Online Resource
    Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region
    Springer Science and Business Media LLC ; 2020
    In:  Scientific Reports Vol. 10, No. 1 ( 2020-10-05)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2020-10-05)
    Abstract: Manual count of mitotic figures, which is determined in the tumor region with the highest mitotic activity, is a key parameter of most tumor grading schemes. It can be, however, strongly dependent on the area selection due to uneven mitotic figure distribution in the tumor section. We aimed to assess the question, how significantly the area selection could impact the mitotic count, which has a known high inter-rater disagreement. On a data set of 32 whole slide images of H & E-stained canine cutaneous mast cell tumor, fully annotated for mitotic figures, we asked eight veterinary pathologists (five board-certified, three in training) to select a field of interest for the mitotic count. To assess the potential difference on the mitotic count, we compared the mitotic count of the selected regions to the overall distribution on the slide. Additionally, we evaluated three deep learning-based methods for the assessment of highest mitotic density: In one approach, the model would directly try to predict the mitotic count for the presented image patches as a regression task. The second method aims at deriving a segmentation mask for mitotic figures, which is then used to obtain a mitotic density. Finally, we evaluated a two-stage object-detection pipeline based on state-of-the-art architectures to identify individual mitotic figures. We found that the predictions by all models were, on average, better than those of the experts. The two-stage object detector performed best and outperformed most of the human pathologists on the majority of tumor cases. The correlation between the predicted and the ground truth mitotic count was also best for this approach (0.963–0.979). Further, we found considerable differences in position selection between pathologists, which could partially explain the high variance that has been reported for the manual mitotic count. To achieve better inter-rater agreement, we propose to use a computer-based area selection for support of the pathologist in the manual mitotic count.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-020-73246-2
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2615211-3
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  • 2
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    Intraoperative free margins assessment of oropharyngeal squamous cell carcinoma with confocal laser endomicroscopy: a pilot study
    Springer Science and Business Media LLC ; 2021
    In:  European Archives of Oto-Rhino-Laryngology Vol. 278, No. 11 ( 2021-11), p. 4433-4439
    Details
    In: European Archives of Oto-Rhino-Laryngology, Springer Science and Business Media LLC, Vol. 278, No. 11 ( 2021-11), p. 4433-4439
    Abstract: This pilot study aimed to assess the feasibility of intraoperative assessment of safe margins with confocal laser endomicroscopy (CLE) during oropharyngeal squamous cell carcinoma (OPSCC) surgery. Methods We included five consecutive patients confirmed OPSCC and planned tumor resection in September and October 2020. Healthy appearing mucosa in the marginal zone, and the tumor margin, were examined with CLE and biopsy during tumor resection. A total of 12,809 CLE frames were correlated with the gold standard of hematoxylin and eosin staining. Three head and neck surgeons and one pathologist were asked to identify carcinoma in a sample of 169 representative images, blinded to the histological results. Results Healthy mucosa showed epithelium with uniform size and shape with distinct cytoplasmic membranes and regular vessel architecture. CLE optical biopsy of OPSCC demonstrated a disorganized arrangement of variable cellular morphology. We calculated an accuracy, sensitivity, specificity, PPV, and NPV of 86%, 90%, 79%, 88%, and 82%, respectively, with inter-rater reliability and κ -value of 0.60. Conclusion CLE can be easily integrated into the intraoperative setting, generate real-time, in-vivo microscopic images of the oropharynx for evaluation and demarcation of cancer. It can eventually contribute to a less radical approach by enabling a more precise evaluation of the cancer margin.
    Type of Medium: Online Resource
    ISSN: 0937-4477 , 1434-4726
    URL: Article
    DOI: 10.1007/s00405-021-06659-y
    RVK:
    XA 26650
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 1459042-6
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  • 3
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    Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images
    Springer Science and Business Media LLC ; 2019
    In:  International Journal of Computer Assisted Radiology and Surgery Vol. 14, No. 1 ( 2019-1), p. 31-42
    Details
    In: International Journal of Computer Assisted Radiology and Surgery, Springer Science and Business Media LLC, Vol. 14, No. 1 ( 2019-1), p. 31-42
    Type of Medium: Online Resource
    ISSN: 1861-6410 , 1861-6429
    URL: Article
    DOI: 10.1007/s11548-018-1836-1
    RVK:
    XA 49910
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
    detail.hit.zdb_id: 2235881-X
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  • 4
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    Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides
    Springer Science and Business Media LLC ; 2020
    In:  Scientific Reports Vol. 10, No. 1 ( 2020-08-03)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2020-08-03)
    Abstract: Exercise-induced pulmonary hemorrhage (EIPH) is a common condition in sport horses with negative impact on performance. Cytology of bronchoalveolar lavage fluid by use of a scoring system is considered the most sensitive diagnostic method. Macrophages are classified depending on the degree of cytoplasmic hemosiderin content. The current gold standard is manual grading, which is however monotonous and time-consuming. We evaluated state-of-the-art deep learning-based methods for single cell macrophage classification and compared them against the performance of nine cytology experts and evaluated inter- and intra-observer variability. Additionally, we evaluated object detection methods on a novel data set of 17 completely annotated cytology whole slide images (WSI) containing 78,047 hemosiderophages. Our deep learning-based approach reached a concordance of 0.85, partially exceeding human expert concordance (0.68 to 0.86, mean of 0.73, SD of 0.04). Intra-observer variability was high (0.68 to 0.88) and inter-observer concordance was moderate (Fleiss’ kappa = 0.67). Our object detection approach has a mean average precision of 0.66 over the five classes from the whole slide gigapixel image and a computation time of below two minutes. To mitigate the high inter- and intra-rater variability, we propose our automated object detection pipeline, enabling accurate, reproducible and quick EIPH scoring in WSI.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-020-65958-2
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2615211-3
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  • 5
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    Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset
    Springer Science and Business Media LLC ; 2022
    In:  Scientific Data Vol. 9, No. 1 ( 2022-09-27)
    Details
    In: Scientific Data, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2022-09-27)
    Abstract: Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. However, many of these supervised algorithms require a large amount of annotated data for robust development. We present a publicly available dataset of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes, including seven cutaneous tumor subtypes. In inter-rater experiments, we show a high consistency of the provided labels, especially for tumor annotations. We further validate the dataset by training a deep neural network for the task of tissue segmentation and tumor subtype classification. We achieve a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in particular. For classification, we achieve a slide-level accuracy of 0.9857. Since canine cutaneous tumors possess various histologic homologies to human tumors the added value of this dataset is not limited to veterinary pathology but extends to more general fields of application.
    Type of Medium: Online Resource
    ISSN: 2052-4463
    URL: Article
    DOI: 10.1038/s41597-022-01692-w
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2775191-0
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  • 6
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    A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor
    Springer Science and Business Media LLC ; 2019
    In:  Scientific Data Vol. 6, No. 1 ( 2019-11-21)
    Details
    In: Scientific Data, Springer Science and Business Media LLC, Vol. 6, No. 1 ( 2019-11-21)
    Abstract: We introduce a novel, large-scale dataset for microscopy cell annotations. The dataset includes 32 whole slide images (WSI) of canine cutaneous mast cell tumors, selected to include both low grade cases as well as high grade cases. The slides have been completely annotated for mitotic figures and we provide secondary annotations for neoplastic mast cells, inflammatory granulocytes, and mitotic figure look-alikes. Additionally to a blinded two-expert manual annotation with consensus, we provide an algorithm-aided dataset, where potentially missed mitotic figures were detected by a deep neural network and subsequently assessed by two human experts. We included 262,481 annotations in total, out of which 44,880 represent mitotic figures. For algorithmic validation, we used a customized RetinaNet approach, followed by a cell classification network. We find F1-Scores of 0.786 and 0.820 for the manually labelled and the algorithm-aided dataset, respectively. The dataset provides, for the first time, WSIs completely annotated for mitotic figures and thus enables assessment of mitosis detection algorithms on complete WSIs as well as region of interest detection algorithms.
    Type of Medium: Online Resource
    ISSN: 2052-4463
    URL: Article
    DOI: 10.1038/s41597-019-0290-4
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
    detail.hit.zdb_id: 2775191-0
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  • 7
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    Inter-species cell detection - datasets on pulmonary hemosiderophages in equine, human and feline specimens
    Springer Science and Business Media LLC ; 2022
    In:  Scientific Data Vol. 9, No. 1 ( 2022-06-03)
    Details
    In: Scientific Data, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2022-06-03)
    Abstract: Pulmonary hemorrhage (P-Hem) occurs among multiple species and can have various causes. Cytology of bronchoalveolar lavage fluid (BALF) using a 5-tier scoring system of alveolar macrophages based on their hemosiderin content is considered the most sensitive diagnostic method. We introduce a novel, fully annotated multi-species P-Hem dataset, which consists of 74 cytology whole slide images (WSIs) with equine, feline and human samples. To create this high-quality and high-quantity dataset, we developed an annotation pipeline combining human expertise with deep learning and data visualisation techniques. We applied a deep learning-based object detection approach trained on 17 expertly annotated equine WSIs, to the remaining 39 equine, 12 human and 7 feline WSIs. The resulting annotations were semi-automatically screened for errors on multiple types of specialised annotation maps and finally reviewed by a trained pathologist. Our dataset contains a total of 297,383 hemosiderophages classified into five grades. It is one of the largest publicly available WSIs datasets with respect to the number of annotations, the scanned area and the number of species covered.
    Type of Medium: Online Resource
    ISSN: 2052-4463
    URL: Article
    DOI: 10.1038/s41597-022-01389-0
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2775191-0
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  • 8
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    Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning
    Springer Science and Business Media LLC ; 2017
    In:  Scientific Reports Vol. 7, No. 1 ( 2017-09-20)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 7, No. 1 ( 2017-09-20)
    Abstract: Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, sufficient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and a reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC in situ . We present and evaluate a novel automatic approach for OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3% (sensitivity 86.6%, specificity 90%).
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-017-12320-8
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
    detail.hit.zdb_id: 2615211-3
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  • 9
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    Segmentation metric misinterpretations in bioimage analysis
    Springer Science and Business Media LLC ; 2023
    In:  Nature Methods
    Details
    In: Nature Methods, Springer Science and Business Media LLC
    Abstract: Quantitative evaluation of image segmentation algorithms is crucial in the field of bioimage analysis. The most common assessment scores, however, are often misinterpreted and multiple definitions coexist with the same name. Here we present the ambiguities of evaluation metrics for segmentation algorithms and show how these misinterpretations can alter leaderboards of influential competitions. We also propose guidelines for how the currently existing problems could be tackled.
    Type of Medium: Online Resource
    ISSN: 1548-7091 , 1548-7105
    URL: Article
    DOI: 10.1038/s41592-023-01942-8
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2163081-1
    SSG: 12
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  • 10
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    Impact of intraepithelial capillary loops and atypical vessels in confocal laser endomicroscopy for the diagnosis of laryngeal and hypopharyngeal squamous cell carcinoma
    Springer Science and Business Media LLC ; 2022
    In:  European Archives of Oto-Rhino-Laryngology Vol. 279, No. 4 ( 2022-04), p. 2029-2037
    Details
    In: European Archives of Oto-Rhino-Laryngology, Springer Science and Business Media LLC, Vol. 279, No. 4 ( 2022-04), p. 2029-2037
    Abstract: Confocal laser endomicroscopy (CLE) allows surface imaging of the laryngeal and pharyngeal mucosa in vivo at a thousand-fold magnification. This study aims to compare irregular blood vessels and intraepithelial capillary loops in healthy mucosa and squamous cell carcinoma (SCC) via CLE. Materials and methods We included ten patients with confirmed SCC and planned total laryngectomy in this study between March 2020 and February 2021. CLE images of these patients were collected and compared with the corresponding histology in hematoxylin and eosin staining. We analyzed the characteristic endomicroscopic patterns of blood vessels and intraepithelial capillary loops for the diagnosis of SCC. Results In a total of 54 sequences, we identified 243 blood vessels which were analyzed regarding structure, diameter, and Fluorescein leakage, confirming that irregular, corkscrew-like vessels (24.4% vs. 1.3%; P   〈  .001), dilated intraepithelial capillary loops (90.8% vs. 28.7%; P   〈  .001), and increased capillary leakage (40.7% vs. 2.5%; P   〈  .001), are significantly more frequently detected in SCC compared to the healthy epithelium. We defined a vessel diameter of 30 μm in capillary loops as a cut-off value, obtaining a sensitivity, specificity, PPV, and NPV and accuracy of 90.6%, 71.3%, 57.4%, 94.7%, and 77.1%, respectively, for the detection of malignancy based solely on capillary architecture. Conclusion Capillaries within malignant lesions are fundamentally different from those in healthy mucosa regions. The capillary architecture is a significant feature aiding the identification of malignant mucosa areas during in-vivo, real-time CLE examination.
    Type of Medium: Online Resource
    ISSN: 0937-4477 , 1434-4726
    URL: Article
    DOI: 10.1007/s00405-021-06954-8
    RVK:
    XA 26650
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 1459042-6
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