Automated analysis of haematopoietic cells in bone marrow microscopy images
| Autor: | Gräbel, Philipp |
|---|---|
| Přispěvatelé: | Merhof, Dorit, Nattkemper, Tim W. |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Aachen : RWTH Aachen University 1 Online-Ressource : Illustrationen, Diagramme (2022). doi:10.18154/RWTH-2023-01572 = Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2022 |
| DOI: | 10.18154/rwth-2023-01572 |
| Popis: | Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2022; Aachen : RWTH Aachen University 1 Online-Ressource : Illustrationen, Diagramme (2023). = Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2022 Traditionally, haematologists diagnose haematopoietic diseases, such as leukaemia, based on the distribution of cell types in bone marrow microscopy images. The automation of this procedure is beneficial in multiple aspects: higher throughput, objective results, and a larger number of cells considered in the statistical analysis of the cell distribution. In this dissertation, dedicated solutions to a wide range of challenges offered by haematopoietic cell data are presented. First, limitations with state-of-the-art cell detection and classification approaches are addressed, for example through Circular Anchors, which considerably reduce the number of false positives during cell detection. This is achieved through better cell representations with circular instead of rectangular anchors. Furthermore, several techniques to incorporate weak annotations are proposed along with novel techniques for reliable cell detection using U-Net. Additionally, this dissertation proposes problem-specific techniques to solve various challenges of haematopoietic cell data. These challenges include visual variabilities caused by inhomogeneities in the staining process and the ordinal aspect of maturity stages of cells within one lineage. In addition, improvements to state-of-the-art methods for semi-supervised learning, which enable new approaches for network pre-training, are presented. Another significant contribution are the novel embedding learning techniques, which incorporate domain knowledge into neural network training. This dissertation introduces the idea of Embedding Guides, which encode different types of cells as points in a two-dimensional vector space. These are utilised by novel methods that enforce such guides in the training of suitable embedding spaces. Other novel methodologies proposed are Spatial Maturity Regression, a regularisation of the embedding space for the maturity in individual cell lineages, and Automatic Latent Interventions, a technique for generic improvements of the embedding space. The proposed methods show considerable improvements of raw classification accuracy and/or secondary scores, which are highly relevant for diagnoses. The approaches developed here suggest that a framework for the automated analysis of bone marrow microscopy images can be developed. This dissertation also includes a discussion on the suitability of automatic analyses in clinical practice as well as considerations for a tool designed for this purpose. Published by RWTH Aachen University, Aachen |
| Databáze: | OpenAIRE |
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