Detection of lobular structures in normal breast tissue

Apou, Grégory; Schaadt, Nadine S.; Naegel, Benoît; Forestier, Germain; Schönmeyer, Ralf; Feuerhake, Friedrich; Wemmert, Cédric; Grote, Anne

doi:10.1016/j.compbiomed.2016.05.004

Cited by 21 publications

(14 citation statements)

References 34 publications

(33 reference statements)

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“…The first aspect was addressed by a three-dimensional (3D) histology reconstruction of serial H&E sections, performed using the Voloom software (microDimensions, Munich, Table 1 Composition of the reduction mammoplasty patient cohort and menstrual cycle phases. (*): for 5 patients in the luteal and 2 patients in the follicular phase of natural cycles (without oral contraceptives), the assignment to a cycle phase was a "best guess"-approximation due to reportedly irregular menses, (**): parity unknown for 1 patient Table 2 Total size of evaluated lobular area within the breast tissue (in cm 2 ) per patient after automated detection [3] and quality assessment of ROIs. "StdDev" = standard deviation.…”

Section: Image Analysismentioning

confidence: 99%

“…In order to quantify immune cell numbers in lobular structures, we developed a modular workflow (see Fig. 1), combining automated regions of interest (ROIs) detection [3], high-resolution cell detection with a robust analysis module for nucleus detection (part of the Tissue Phenomics framework, Definiens AG, Munich, Germany) [5], and subsequent cell classification. For quality control, all automatically detected ROIs were verified by visual inspection of an expert.…”

Section: Stainingmentioning

confidence: 99%

“…This addresses the emerging challenge of comprehensive immune cell evaluation in the era of oncoimmunology and paves the way for computer-aided breast cancer biopsy analysis. Therefore, we acquired largescale spatial data on immune cells in WSIs by combining novel technologies [3,36] in a modular approach in breast tissue of healthy women who underwent reduction mammoplasty and relate the image-based data with the hormonal status of the patients at time of surgery.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Image analysis of immune cell patterns in the human mammary gland during the menstrual cycle refines lymphocytic lobulitis

Schaadt

Alfonso

Schönmeyer³

et al. 2017

Breast Cancer Res Treat

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Section: Image Analysismentioning

confidence: 99%

Section: Stainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Image analysis of immune cell patterns in the human mammary gland during the menstrual cycle refines lymphocytic lobulitis

Schaadt

Alfonso

Schönmeyer³

et al. 2017

Breast Cancer Res Treat

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“…In order to provide reproducible, observer-independent, and quantitative evaluation of immune infiltrates, image analysis approaches could automatically detect regions of interest (ROIs) [20][21][22] and classify cells based on immunohistochemistry [23]. The increasing medical need for evaluation of particular cell types in anatomically or immunologically defined ROIs falls into an era of massive advance in machine learning (ML), with deep learning and pixel-based ML (as opposed to feature-based approaches to ML) holding great promise for identification, classification, and quantitative assessment of relevant patterns in medical images including microscopy [24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Graph-based description of tertiary lymphoid organs at single-cell level

Schaadt

Schönmeyer²,

Forestier

et al. 2020

PLoS Comput Biol

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Our aim is to complement observer-dependent approaches of immune cell evaluation in microscopy images with reproducible measures for spatial composition of lymphocytic infiltrates. Analyzing such patterns of inflammation is becoming increasingly important for therapeutic decisions, for example in transplantation medicine or cancer immunology. We developed a graph-based assessment of lymphocyte clustering in full whole slide images. Based on cell coordinates detected in the full image, a Delaunay triangulation and distance criteria are used to build neighborhood graphs. The composition of nodes and edges are used for classification, e.g. using a support vector machine. We describe the variability of these infiltrates on CD3/CD20 duplex staining in renal biopsies of long-term functioning allografts, in breast cancer cases, and in lung tissue of cystic fibrosis patients. The assessment includes automated cell detection, identification of regions of interest, and classification of lymphocytic clusters according to their degree of organization. We propose a neighborhood feature which considers the occurrence of edges with a certain type in the graph to distinguish between phenotypically different immune infiltrates. Our work addresses a medical need and provides a scalable framework that can be easily adjusted to the requirements of different research questions. Author summaryIn this study, we developed a workflow to detect and classify immune infiltrates in gigapixel microscopy images. It allowed us to measure the degree of organization in lymphocyte clusters with graph-based features and finally to distinguish between tertiary lymphoid organs and other infiltrates. As a clinically relevant use case, we applied it to three different types of tissues and diseases. Our method addresses the need for observer-independent, large-scale evaluation of immune cell patterns and is a prerequisite to capture PLOS Computational Biology | https://doi.

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“…Recent examples include works on stacked sparse auto encoders for nuclei detection [29] and on hierarchical learning [14]. Regarding the assessment of glandular and tubular structures, there are only a few works in the field of breast cancer, such as [9] or [2]. However, for a general overview on the computational assessment of relevant pathological structures and primitives, we refer the interested reader to [15].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Breast Cancer Histology Using Densely Connected Convolutional Networks

Kohl¹,

Walz

Ludwig³

et al. 2018

Lecture Notes in Computer Science

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Breast cancer is the most frequently diagnosed cancer and leading cause of cancer-related death among females worldwide. In this article, we investigate the applicability of densely connected convolutional neural networks to the problems of histology image classification and whole slide image segmentation in the area of computer-aided diagnoses for breast cancer. To this end, we study various approaches for transfer learning and apply them to the data set from the 2018 grand challenge on breast cancer histology images (BACH).Keywords: digital pathology, breast cancer, deep learning 1. mitotic activity as a measure of cellular proliferation, 2. nuclear pleomorphism, i.e. how different the tumor cells are in comparison to normal cells, and 3. glandular and tubular differentiation, i.e. how well the tumor resembles normal structures.Current developments in the area of digital pathology are driven by the observation that genetic and phenotypic intra-tumor heterogeneity have a direct impact on both diagnosis and disease management [18] as well as the availability of effective machine learning techniques, such as deep convolutional neural networks. Particularly the segmentation of WSIs, i.e. the second part of the BACH challenge, plays an increasingly important role as it facilitates not only a standardized assessment of resection margins, but also novel scoring approaches, such as the ImmunoScore [11], and a better understanding of tumor heterogeneity and micro-environment, e.g. via phenotype-guided genetic readouts.

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Detection of lobular structures in normal breast tissue

Abstract: Different approaches of automated ROI detection are feasible and should be selected according to the individual needs of biomarker research. Additionally, detected ROIs could be used as a basis for quantification of immune infiltration in lobular structures.

Cited by 21 publications

References 34 publications

Image analysis of immune cell patterns in the human mammary gland during the menstrual cycle refines lymphocytic lobulitis

Image analysis of immune cell patterns in the human mammary gland during the menstrual cycle refines lymphocytic lobulitis

Graph-based description of tertiary lymphoid organs at single-cell level

Assessment of Breast Cancer Histology Using Densely Connected Convolutional Networks

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