MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images

The analysis of glandular morphology within colon histopathology images is an important step in determining the grade of colon cancer. Despite the importance of this task, manual segmentation is laborious, time-consuming and can suffer from subjectivity among pathologists. The rise of computational pathology has led to the development of automated methods for gland segmentation that aim to overcome the challenges of manual segmentation. However, this task is non-trivial due to the large variability in glandular appearance and the difficulty in differentiating between certain glandular and non-glandular histological structures. Furthermore, a measure of uncertainty is essential for diagnostic decision making. To address these challenges, we propose a fully convolutional neural network that counters the loss of information caused by max-pooling by re-introducing the original image at multiple points within the network. We also use atrous spatial pyramid pooling with varying dilation rates for preserving the resolution and multi-level aggregation. To incorporate uncertainty, we introduce random transformations during test time for an enhanced segmentation result that simultaneously generates an uncertainty map, highlighting areas of ambiguity. We show that this map can be used to define a metric for disregarding predictions with high uncertainty. The proposed network achieves state-of-the-art performance on the GlaS challenge dataset and on a second independent colorectal adenocarcinoma dataset. In addition, we perform gland instance segmentation on whole-slide images from two further datasets to highlight the generalisability of our method. As an extension, we introduce MILD-Net+ for simultaneous gland and lumen segmentation, to increase the diagnostic power of the network.Comment: Initial version published at Medical Imaging with Deep Learning (MIDL) 201

Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images

Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. This becomes particularly challenging for extremely large images, since manual intervention and processing time can make segmentation intractable. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional (3D) contour evolution that extends previous work on fast two-dimensional active contours. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell segmentation tasks when compared to existing methods on large 3D brain images

Monte-Carlo sampling applied to multiple instance learning for whole slide image classification

In this paper we propose a patch sampling strategy based on sequential Monte-Carlo methods for Whole Slide Image classification in the context of Multiple Instance Learning and show its capability to achieve high generalization performance on the differentiation between sun exposed and not sun exposed pieces of skin tissue.Postprint (published version

Deep active learning for suggestive segmentation of biomedical image stacks via optimisation of Dice scores and traced boundary length

Manual segmentation of stacks of 2D biomedical images (e.g., histology) is a time-consuming task which can be sped up with semi-automated techniques. In this article, we present a suggestive deep active learning framework that seeks to minimise the annotation effort required to achieve a certain level of accuracy when labelling such a stack. The framework suggests, at every iteration, a specific region of interest (ROI) in one of the images for manual delineation. Using a deep segmentation neural network and a mixed cross-entropy loss function, we propose a principled strategy to estimate class probabilities for the whole stack, conditioned on heterogeneous partial segmentations of the 2D images, as well as on weak supervision in the form of image indices that bound each ROI. Using the estimated probabilities, we propose a novel active learning criterion based on predictions for the estimated segmentation performance and delineation effort, measured with average Dice scores and total delineated boundary length, respectively, rather than common surrogates such as entropy. The query strategy suggests the ROI that is expected to maximise the ratio between performance and effort, while considering the adjacency of structures that may have already been labelled – which decrease the length of the boundary to trace. We provide quantitative results on synthetically deformed MRI scans and real histological data, showing that our framework can reduce labelling effort by up to 60–70% without compromising accuracy

Classification of target tissues of Eisenia fetida using sequential multimodal chemical analysis and machine learning

Acquiring comprehensive knowledge about the uptake of pollutants, impact on tissue integrity and the effects at the molecular level in organisms is of increasing interest due to the environmental exposure to numerous contaminants. The analysis of tissues can be performed by histological examination, which is still time-consuming and restricted to target-specific staining methods. The histological approaches can be complemented with chemical imaging analysis. Chemical imaging of tissue sections is typically performed using a single imaging approach. However, for toxicological testing of environmental pollutants, a multimodal approach combined with improved data acquisition and evaluation is desirable, since it may allow for more rapid tissue characterization and give further information on ecotoxicological effects at the tissue level. Therefore, using the soil model organism Eisenia fetida as a model, we developed a sequential workflow combining Fourier transform infrared spectroscopy (FTIR) and matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) for chemical analysis of the same tissue sections. Data analysis of the FTIR spectra via random decision forest (RDF) classification enabled the rapid identification of target tissues (e.g., digestive tissue), which are relevant from an ecotoxicological point of view. MALDI imaging analysis provided specific lipid species which are sensitive to metabolic changes and environmental stressors. Taken together, our approach provides a fast and reproducible workflow for label-free histochemical tissue analyses in E. fetida, which can be applied to other model organisms as well. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00418-021-02037-1

Design and development of optical reflectance spectroscopy and optical coherence tomography catheters for myocardial tissue characterization

Catheter ablation therapy attempts to restore sinus rhythm in arrhythmia patients by producing site-specific tissue modification along regions which cause abnormal electrical activity. This treatment, though widely used, often requires repeat procedures to observe long-term therapeutic benefits. This limitation is driven in part by challenges faced by conventional schemes in validating lesion adequacy at the time of the procedure. Optical techniques are well-suited for the interrogation and characterization of biological tissues. In particular, optical coherence tomography (OCT) relies on coherence gating of singly-scattered light to enable high-resolution structural imaging for tissue diagnostics and procedural guidance. Alternatively, optical reflectance spectroscopy (ORS) is a point measurement technique which makes use of incoherent, multiply-scattered light to probe tissue volumes and derive important data from its optical signature. ORS relies on the fact that light-tissue interactions are regulated by absorption and scattering, which directly relate to the intrinsic tissue biochemistry and cellular organization. In this thesis, we explore the integration of these modalities into ablation catheters for obtaining procedural metrics which could be utilized to guide catheter ablation therapy. We first present the development of an accelerated computational light transport model and its application for guiding ORS catheter design. A custom ORS-integrated ablation catheter is then implemented and tested within porcine specimens in vitro. A model is proposed for real-time estimation of lesion size based on changes in spectral morphology acquired during ablation. We then fabricated custom integrated OCT M-mode RF catheters and present a model for detecting contact status based on deep convolutional neural networks trained on endomyocardial images. Additionally, we demonstrate for the first time, tracking of RF-induced lesion formation employing OCT Doppler micro-velocimetry; this response is shown to be commensurate with the degree of treatment. We further demonstrate for the first time spectroscopic tracking of kinetics related to the heme oxidation cascade during thermal treatment, which are linked to tissue denaturation. The pairing of these modalities into a single RF catheter was also validated for guiding lesion delivery in vitro and within live pigs. Finally, we conclude with a proof-of-concept demonstration of ORS as a mapping tool to guide epicardial ablation in human donor hearts. These results showcase the vast potential of ORS and OCT empowered RF catheters for aiding intraprocedural guidance of catheter ablation procedures which could be utilized alongside current practices