| 1. |
- Andersson, Mariam, et al.
(författare)
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Axon morphology is modulated by the local environment and impacts the noninvasive investigation of its structure-function relationship
- 2020
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 1091-6490. ; 117:52, s. 33649-33659
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Tidskriftsartikel (refereegranskat)abstract
- Axonal conduction velocity, which ensures efficient function of the brain network, is related to axon diameter. Noninvasive, in vivo axon diameter estimates can be made with diffusion magnetic resonance imaging, but the technique requires three-dimensional (3D) validation. Here, high-resolution, 3D synchrotron X-ray nano-holotomography images of white matter samples from the corpus callosum of a monkey brain reveal that blood vessels, cells, and vacuoles affect axonal diameter and trajectory. Within single axons, we find that the variation in diameter and conduction velocity correlates with the mean diameter, contesting the value of precise diameter determination in larger axons. These complex 3D axon morphologies drive previously reported 2D trends in axon diameter and g-ratio. Furthermore, we find that these morphologies bias the estimates of axon diameter with diffusion magnetic resonance imaging and, ultimately, impact the investigation and formulation of the axon structure-function relationship.
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| 2. |
- Anklin, Valentin, et al.
(författare)
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Learning Whole-Slide Segmentation from Inexact and Incomplete Labels Using Tissue Graphs
- 2021
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Ingår i: Medical Image Computing and Computer Assisted Intervention. - Cham : Springer Nature. - 9783030871956 - 9783030871963 ; , s. 636-646
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Konferensbidrag (refereegranskat)abstract
- Segmenting histology images into diagnostically relevant regions is imperative to support timely and reliable decisions by pathologists. To this end, computer-aided techniques have been proposed to delineate relevant regions in scanned histology slides. However, the techniques necessitate task-specific large datasets of annotated pixels, which is tedious, time-consuming, expensive, and infeasible to acquire for many histology tasks. Thus, weakly-supervised semantic segmentation techniques are proposed to leverage weak supervision which is cheaper and quicker to acquire. In this paper, we propose SEGGINI, a weakly-supervised segmentation method using graphs, that can utilize weak multiplex annotations, i.e., inexact and incomplete annotations, to segment arbitrary and large images, scaling from tissue microarray (TMA) to whole slide image (WSI). Formally, SEGGINI constructs a tissue-graph representation for an input image, where the graph nodes depict tissue regions. Then, it performs weakly-supervised segmentation via node classification by using inexact image-level labels, incomplete scribbles, or both. We evaluated SEGGINI on two public prostate cancer datasets containing TMAs and WSIs. Our method achieved state-of-the-art segmentation performance on both datasets for various annotation settings while being comparable to a pathologist baseline. Code and models are available at: https://github.com/histocartography/seg-gini
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| 3. |
- Cammoun, Leila, et al.
(författare)
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A Review of Tensors and Tensor Signal Processing
- 2009. - 1
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Ingår i: Tensors in Image Processing and Computer Vision. - London : Springer. - 9781848822986 ; , s. 1-32
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Tensors have been broadly used in mathematics and physics, since they are a generalization of scalars or vectors and allow to represent more complex properties. In this chapter we present an overview of some tensor applications, especially those focused on the image processing field. From a mathematical point of view, a lot of work has been developed about tensor calculus, which obviously is more complex than scalar or vectorial calculus. Moreover, tensors can represent the metric of a vector space, which is very useful in the field of differential geometry. In physics, tensors have been used to describe several magnitudes, such as the strain or stress of materials. In solid mechanics, tensors are used to define the generalized Hooke’s law, where a fourth order tensor relates the strain and stress tensors. In fluid dynamics, the velocity gradient tensor provides information about the vorticity and the strain of the fluids. Also an electromagnetic tensor is defined, that simplifies the notation of the Maxwell equations. But tensors are not constrained to physics and mathematics. They have been used, for instance, in medical imaging, where we can highlight two applications: the diffusion tensor image, which represents how molecules diffuse inside the tissues and is broadly used for brain imaging; and the tensorial elastography, which computes the strain and vorticity tensor to analyze the tissues properties. Tensors have also been used in computer vision to provide information about the local structure or to define anisotropic image filters.
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| 4. |
- Jaume, Guillaume, et al.
(författare)
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Quantifying Explainers of Graph Neural Networks in Computational Pathology
- 2021
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Ingår i: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665445092 - 9781665445108 ; , s. 8102-8112
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Konferensbidrag (refereegranskat)abstract
- Explainability of deep learning methods is imperative to facilitate their clinical adoption in digital pathology. However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities' notion, thus complicating comprehension by pathologists. In this work, we address this by adopting biological entity-based graph processing and graph explainers enabling explanations accessible to pathologists. In this context, a major challenge becomes to discern meaningful explainers, particularly in a standardized and quantifiable fashion. To this end, we propose herein a set of novel quantitative metrics based on statistics of class separability using pathologically measurable concepts to characterize graph explainers. We employ the proposed metrics to evaluate three types of graph explainers, namely the layer-wise relevance propagation, gradient-based saliency, and graph pruning approaches, to explain Cell-Graph representations for Breast Cancer Subtyping. The proposed metrics are also applicable in other domains by using domain-specific intuitive concepts. We validate the qualitative and quantitative findings on the BRACS dataset, a large cohort of breast cancer RoIs, by expert pathologists.
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| 5. |
- Pati, Pushpak, et al.
(författare)
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Hierarchical graph representations in digital pathology
- 2022
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Ingår i: Medical Image Analysis. - : Elsevier. - 1361-8415 .- 1361-8423. ; 75
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Tidskriftsartikel (refereegranskat)abstract
- Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological entities is imperative for computer aided cancer patient care. To this end, several approaches have leveraged cell-graphs, capturing the cell-microenvironment, to depict the tissue. These allow for utilizing graph theory and machine learning to map the tissue representation to tissue functionality, and quantify their relationship. Though cellular information is crucial, it is incomplete alone to comprehensively characterize complex tissue structure. We herein treat the tissue as a hierarchical composition of multiple types of histological entities from fine to coarse level, capturing multivariate tissue information at multiple levels. We propose a novel multi-level hierarchical entity graph representation of tissue specimens to model the hierarchical compositions that encode histological entities as well as their intra-and inter-entity level interactions. Subsequently, a hierarchical graph neural network is proposed to operate on the hierarchical entity-graph and map the tissue structure to tissue functionality. Specifically, for input histology images, we utilize well-defined cells and tissue regions to build HierArchical Cell-to-Tissue ( HACT ) graph representations, and devise HACT-Net, a message passing graph neural network, to classify the HACT representations. As part of this work, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of Haematoxylin & Eosin stained breast tumor regions-of-interest, to evaluate and benchmark our proposed methodology against pathologists and state-of-the-art computer-aided diagnostic approaches. Through comparative assessment and ablation studies, our proposed method is demonstrated to yield superior classification results compared to alternative methods as well as individual pathologists. The code, data, and models can be accessed at https://github.com/histocartography/hact-net . (c) 2021 Elsevier B.V. All rights reserved.
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