HoloLens for medical imaging using post-mortem fetal micro-CT data

AIM AND OBJECTIVES: Demonstrate applicability of HoloLens technology for viewing post-mortem fetal micro-CT imaging data. Develop a pipeline focusing on the required editing of 3D segmentations for rendering in virtual reality (VR), file format and storage needs for medical holographic applications and the necessary functionality of a holographic application interface

A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation

Cellular electron cryo-tomography enables the 3D visualization of cellular organization in the near-native state and at submolecular resolution. However, the contents of cellular tomograms are often complex, making it difficult to automatically isolate different in situ cellular components. In this paper, we propose a convolutional autoencoder-based unsupervised approach to provide a coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate that the autoencoder can be used for efficient and coarse characterization of features of macromolecular complexes and surfaces, such as membranes. In addition, the autoencoder can be used to detect non-cellular features related to sample preparation and data collection, such as carbon edges from the grid and tomogram boundaries. The autoencoder is also able to detect patterns that may indicate spatial interactions between cellular components. Furthermore, we demonstrate that our autoencoder can be used for weakly supervised semantic segmentation of cellular components, requiring a very small amount of manual annotation.Comment: Accepted by Journal of Structural Biolog

Plant phenomics, from sensors to knowledge

Major improvements in crop yield are needed to keep pace with population growth and climate change. While plant breeding efforts have greatly benefited from advances in genomics, profiling the crop phenome (i.e., the structure and function of plants) associated with allelic variants and environments remains a major technical bottleneck. Here, we review the conceptual and technical challenges facing plant phenomics. We first discuss how, given plants’ high levels of morphological plasticity, crop phenomics presents distinct challenges compared with studies in animals. Next, we present strategies for multi-scale phenomics, and describe how major improvements in imaging, sensor technologies and data analysis are now making high-throughput root, shoot, whole-plant and canopy phenomic studies possible. We then suggest that research in this area is entering a new stage of development, in which phenomic pipelines can help researchers transform large numbers of images and sensor data into knowledge, necessitating novel methods of data handling and modelling. Collectively, these innovations are helping accelerate the selection of the next generation of crops more sustainable and resilient to climate change, and whose benefits promise to scale from physiology to breeding and to deliver real world impact for ongoing global food security efforts

ITErRoot: High Throughput Segmentation of 2-Dimensional Root System Architecture

Root system architecture (RSA) analysis is a form of high-throughput plant phenotyping which has recently benefited from the application of various deep learning techniques. A typical RSA pipeline includes a segmentation step, where the root system is extracted from 2D images. The segmented image is then passed to subsequent steps for processing, which result in some representation of the architectural properties of the root system. This representation is then used for trait computation, which can be used to identify various desirable properties of a plant’s RSA. Errors which arise at the segmentation stage can propagate themselves throughout the remainder of the pipeline and impact results of trait analysis. This work aims to design an iterative neural network architecture, called ITErRoot, which is particularly well suited to the segmentation of root structure from 2D images in the presence of non-root objects. A novel 2D root image dataset is created along with a ground truth annotation tool designed to facilitate consistent manual annotation of RSA. The proposed architecture is able to take advantage of the root structure to obtain a high quality segmentation and is generalizable to root systems with thin roots, showing improved quality over recent approaches to RSA segmentation. We provide rigorous analysis designed to identify the strengths and weaknesses of the proposed model as well as to validate the effectiveness of the approach for producing high-quality segmentations

Automatic lung nodule detection from chest CT data using geometrical features: initial results

In this paper, a complete system for automatic lung nodule detection from Chest CT data is proposed. The proposed system includes the methods of lung segmentation and nodule detection from CT data. The algorithm for lung segmentation consists ofsurrounding air voxel removal, body fat/tissue identification, trachea detection, and pulmonary vessels segmentation. The nodule detection algorithm comprises of candidate surface generation, geometrical feature generation and classification. The proposed system shows 88.2% sensitivity for nodule >=3mm with 8.91 false positive per dataset

An efficient software tool to segment slice and view electron tomograms

Dissertação de mestrado em Computer ScienceSegmentation is a key method to extract useful information in Electron Tomography. Manual segmentation is the most commonly used method, but it is subject to user bias and the process is slow. The lack of adequate automated processes, due to the high complexity and to the low signal-to-noise ratio of these tomograms, provided the main challenges for this dissertation: to develop a software tool to efficiently handle electron tomograms, including a novel 3D segmentation algorithm. Tomograms can be seen as a stack of 2D images; operations on tomograms usually lead to computationally intense tasks. This is due to the large amount of involved data and to the strided and random memory access patterns. These characteristics represent serious problems on novel computing systems, which rely on complex memory hierarchy architectures to hide memory access latency time. A software tool with a user-friendly interface — TomSeg — was designed, implemented and tested with experimental datasets, built with sequences of Scanning Electron Microscopy images obtained using a Slice and View technique. This tool lets users align, crop, segment and export electron tomograms, using computationally efficient processes. TomSeg takes advantage of the most usual architectures of modern compute servers, namely based on multicore and many-core CPU devices, exploring vector and parallel programming techniques; it also explores the available GPU-devices to speedup critical code functions. Validation and performance results on a compute server are presented together with the performance improvements obtained during the implementation and test phases. TomSeg is an open-source tool for Unix and Windows that can be easily extended with new algorithms to efficiently handle generic tomograms.A segmentação é uma técnica fundamental na tomografia eletrónica para a extração de informação. A segmentação manual é o método mais utilizado, mas é um processo lento e sujeito à parcialidade humana. A falta de métodos automáticos adequados, muito devido à elevada complexidade e à baixa relação sinal-ruído destes tomogramas, conduziu aos principais desafios desta dissertação: desenvolver uma ferramenta de software para manusear tomogramas eletrónicos de forma eficiente, que inclui um novo algoritmo de segmentação 3D. Os tomogramas podem ser vistos como uma pilha de imagens 2D; operações sobre tomogramas costumam originar tarefas computacionalmente exigentes. Isto deve-se à grande quantidade de dados envolvidos e aos acessos espaçados e aleatórios à memória. Estas características representam problemas sérios nos mais recentes sistemas de computação, que dependem de uma complexa arquitetura hierárquica para esconder o tempo de acesso à memória. Desenhou-se, implementou-se e testou-se uma ferramenta de software com uma interface de utilização amigável — TomSeg — utilizando conjuntos de dados experimentais, construídos a partir de sequências de imagens de microscopia eletrónica de varrimento obtidas através de uma técnica de Slice and View. Esta ferramenta permite aos utilizadores alinhar, cortar, segmentar e exportar tomogramas eletrónicos, utilizando processos computacionalmente eficientes. O TomSeg tira vantagem das arquiteturas mais habituais dos servidores de computação atuais, nomeadamente daqueles baseados em dispositivos CPU multicore e many-core, explorando técnicas de programação vetorial e paralela; os dispositivos GPU podem ainda ser usados como aceleradores de algumas funções. Vários resultados de validação obtidos num servidor de computação são apresentados, em conjunto com algumas melhorias obtidas durante as fases de implementação e teste. O TomSeg é uma ferramenta de código aberto para Unix e Windows que pode ser estendida facilmente com novos algoritmos para manusear de forma eficiente qualquer tipo de tomogramas

MIA-clustering: a novel method for segmentation of paleontological material

Paleontological research increasingly uses high-resolution micro-computed tomography (μCT) to study the inner architecture of modern and fossil bone material to answer important questions regarding vertebrate evolution. This non-destructive method allows for the measurement of otherwise inaccessible morphology. Digital measurement is predicated on the accurate segmentation of modern or fossilized bone from other structures imaged in μCT scans, as errors in segmentation can result in inaccurate calculations of structural parameters. Several approaches to image segmentation have been proposed with varying degrees of automation, ranging from completely manual segmentation, to the selection of input parameters required for computational algorithms. Many of these segmentation algorithms provide speed and reproducibility at the cost of flexibility that manual segmentation provides. In particular, the segmentation of modern and fossil bone in the presence of materials such as desiccated soft tissue, soil matrix or precipitated crystalline material can be difficult. Here we present a free open-source segmentation algorithm application capable of segmenting modern and fossil bone, which also reduces subjective user decisions to a minimum. We compare the effectiveness of this algorithm with another leading method by using both to measure the parameters of a known dimension reference object, as well as to segment an example problematic fossil scan. The results demonstrate that the medical image analysis-clustering method produces accurate segmentations and offers more flexibility than those of equivalent precision. Its free availability, flexibility to deal with non-bone inclusions and limited need for user input give it broad applicability in anthropological, anatomical, and paleontological contexts

Pore, live root and necromass quantification in complex heterogeneous wetland soils using X-ray computed tomography

Subsurface structures and especially the interactions between pores, roots and other organic matter elements have a strong impact on ecosystem functioning. Yet despite recent progress in the application of X-ray Computed Microtomography (µCT) to soil structure in agricultural science, applications to the more complex and heterogeneous substrates found in natural soils, specifically wetland soils, remain sparse. We apply X-ray µCT to a complex heterogenous soil and develop a robust segmentation method to quantify the pores, live roots and necromass. This approach significantly improves the detection of the organic matter elements, and gives us unprecedented detail and resolution in the segmentation of pores, live roots and necromass at a high spatial resolution (62.5 µm in this study). We identify several situations where pores and organic matter interact in the soil, including the disconnected air spaces (aerenchyma) that run within the Spartina stem and roots, tubular-shaped pores left behind by decaying roots, and lateral roots deploying within structural fragilities in the sediment. The capacity of X-ray µCT to distinguish the connected live root system from the necromass opens possibilities for applications to determine key wetland soil functions such as soil cohesivity, soil nutrient exchanges and soil carbon dynamics