545 research outputs found
Mathematical Morphology for Quantification in Biological & Medical Image Analysis
Mathematical morphology is an established field of image processing first introduced as an application of set and lattice theories. Originally used to characterise particle distributions, mathematical morphology has gone on to be a core tool required for such important analysis methods as skeletonisation and the watershed transform. In this thesis, I introduce a selection of new image analysis techniques based on mathematical morphology.
Utilising assumptions of shape, I propose a new approach for the enhancement of vessel-like objects in images: the bowler-hat transform. Built upon morphological operations, this approach is successful at challenges such as junctions and robust against noise. The bowler-hat transform is shown to give better results than competitor methods on challenging data such as retinal/fundus imagery.
Building further on morphological operations, I introduce two novel methods for particle and blob detection. The first of which is developed in the context of colocalisation, a standard biological assay, and the second, which is based on Hilbert-Edge Detection And Ranging (HEDAR), with regard to nuclei detection and counting in fluorescent microscopy. These methods are shown to produce accurate and informative results for sub-pixel and supra-pixel object counting in complex and noisy biological scenarios.
I propose a new approach for the automated extraction and measurement of object thickness for intricate and complicated vessels, such as brain vascular in medical images. This pipeline depends on two key technologies: semi-automated segmentation by advanced level-set methods and automatic thickness calculation based on morphological operations. This approach is validated and results demonstrating the broad range of challenges posed by these images and the possible limitations of this pipeline are shown.
This thesis represents a significant contribution to the field of image processing using mathematical morphology and the methods within are transferable to a range of complex challenges present across biomedical image analysis
PHT-bot: Deep-Learning based system for automatic risk stratification of COPD patients based upon signs of Pulmonary Hypertension
Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity
and mortality worldwide. Identifying those at highest risk of deterioration
would allow more effective distribution of preventative and surveillance
resources. Secondary pulmonary hypertension is a manifestation of advanced
COPD, which can be reliably diagnosed by the main Pulmonary Artery (PA) to
Ascending Aorta (Ao) ratio. In effect, a PA diameter to Ao diameter ratio of
greater than 1 has been demonstrated to be a reliable marker of increased
pulmonary arterial pressure. Although clinically valuable and readily
visualized, the manual assessment of the PA and the Ao diameters is time
consuming and under-reported. The present study describes a non invasive method
to measure the diameters of both the Ao and the PA from contrast-enhanced chest
Computed Tomography (CT). The solution applies deep learning techniques in
order to select the correct axial slice to measure, and to segment both
arteries. The system achieves test Pearson correlation coefficient scores of
93% for the Ao and 92% for the PA. To the best of our knowledge, it is the
first such fully automated solution
A Comprehensive Overview of Computational Nuclei Segmentation Methods in Digital Pathology
In the cancer diagnosis pipeline, digital pathology plays an instrumental
role in the identification, staging, and grading of malignant areas on biopsy
tissue specimens. High resolution histology images are subject to high variance
in appearance, sourcing either from the acquisition devices or the H\&E
staining process. Nuclei segmentation is an important task, as it detects the
nuclei cells over background tissue and gives rise to the topology, size, and
count of nuclei which are determinant factors for cancer detection. Yet, it is
a fairly time consuming task for pathologists, with reportedly high
subjectivity. Computer Aided Diagnosis (CAD) tools empowered by modern
Artificial Intelligence (AI) models enable the automation of nuclei
segmentation. This can reduce the subjectivity in analysis and reading time.
This paper provides an extensive review, beginning from earlier works use
traditional image processing techniques and reaching up to modern approaches
following the Deep Learning (DL) paradigm. Our review also focuses on the weak
supervision aspect of the problem, motivated by the fact that annotated data is
scarce. At the end, the advantages of different models and types of supervision
are thoroughly discussed. Furthermore, we try to extrapolate and envision how
future research lines will potentially be, so as to minimize the need for
labeled data while maintaining high performance. Future methods should
emphasize efficient and explainable models with a transparent underlying
process so that physicians can trust their output.Comment: 47 pages, 27 figures, 9 table
Three-dimensional tumour microenvironment reconstruction and tumour-immune interactions' analysis
Tumours arise within complex 3D microenvironments, but the routine 2D analysis of tumours often underestimates the spatial heterogeneity. In this paper, we present a methodology to reconstruct and analyse 3D tumour models from routine clinical samples allowing 3D interactions to be analysed at cellular resolution. Our workflow involves cutting thin serial sections of tumours followed by labelling of cells using markers of interest. Serial sections are then scanned, and digital multiplexed data are created for computational reconstruction. Following spectral unmixing, a registration method of the consecutive images based on a pre-alignment, a parametric and a non-parametric image registration step is applied. For the segmentation of the cells, an ellipsoidal model is proposed and for the 3D reconstruction, a cubic interpolation method is used. The proposed 3D models allow us to identify specific interaction patterns that emerge as tumours develop, adapt and evolve within their host microenvironment. We applied our technique to map tumour-immune interactions of colorectal cancer and preliminary results suggest that 3D models better represent the tumor-immune cells interaction revealing mechanisms within the tumour microenvironment and its heterogeneity
Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review
Instance segmentation of nuclei and glands in the histology images is an
important step in computational pathology workflow for cancer diagnosis,
treatment planning and survival analysis. With the advent of modern hardware,
the recent availability of large-scale quality public datasets and the
community organized grand challenges have seen a surge in automated methods
focusing on domain specific challenges, which is pivotal for technology
advancements and clinical translation. In this survey, 126 papers illustrating
the AI based methods for nuclei and glands instance segmentation published in
the last five years (2017-2022) are deeply analyzed, the limitations of current
approaches and the open challenges are discussed. Moreover, the potential
future research direction is presented and the contribution of state-of-the-art
methods is summarized. Further, a generalized summary of publicly available
datasets and a detailed insights on the grand challenges illustrating the top
performing methods specific to each challenge is also provided. Besides, we
intended to give the reader current state of existing research and pointers to
the future directions in developing methods that can be used in clinical
practice enabling improved diagnosis, grading, prognosis, and treatment
planning of cancer. To the best of our knowledge, no previous work has reviewed
the instance segmentation in histology images focusing towards this direction.Comment: 60 pages, 14 figure
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