Volume Segmantics: A Python Package for Semantic
Segmentation of Volumetric Data Using Pre-trained PyTorch Deep Learning
Models

King, Oliver N.; Bellos, Dimitrios; Basham, Mark

doi:10.21105/joss.04691

Cited by 2 publications

References 8 publications

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Charting the molecular landscape of neuronal organisation within the hippocampus using cryo electron tomography

Glynn,

Smith,

Case

et al. 2024

Preprint

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Cellular cryo-electron tomography (cryoET) enables the capture of detailed structural information within a biologically relevant environment. However, information in more complex samples, such as tissues, is lacking. Importantly, these observations need to be set in context of populations; imaging on the molecular scale to-date is limited to few observations in-situ that struggle to be generalised. This is due to limitations in throughput and versatility employed by current instrumentation. Here, we utilise plasma focused ion beam milling to examine the molecular landscape of mouse hippocampus by cryoET in targeted regions across multiple individuals revealing the complex organisation of macromolecules from the CA1 strata pyramidale (sp) to radiatum (sr). Our data represent a molecular atlas, producing snapshots of hippocampal architecture in adult mouse. The combination of instrumentation and application of technical advancements provides a framework to explore specific structural questions within native tissues in a targeted manner.

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Charting the molecular landscape of neuronal organisation within the hippocampus using cryo electron tomography

Glynn,

Smith,

Case

et al. 2024

Preprint

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An Overview of Open Source Deep Learning-Based Libraries for Neuroscience

et al. 2023

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In recent years, deep learning has revolutionized machine learning and its applications, producing results comparable to human experts in several domains, including neuroscience. Each year, hundreds of scientific publications present applications of deep neural networks for biomedical data analysis. Due to the fast growth of the domain, it could be a complicated and extremely time-consuming task for worldwide researchers to have a clear perspective of the most recent and advanced software libraries. This work contributes to clarifying the current situation in the domain, outlining the most useful libraries that implement and facilitate deep learning applications for neuroscience, allowing scientists to identify the most suitable options for their research or clinical projects. This paper summarizes the main developments in deep learning and their relevance to neuroscience; it then reviews neuroinformatic toolboxes and libraries collected from the literature and from specific hubs of software projects oriented to neuroscience research. The selected tools are presented in tables detailing key features grouped by the domain of application (e.g., data type, neuroscience area, task), model engineering (e.g., programming language, model customization), and technological aspect (e.g., interface, code source). The results show that, among a high number of available software tools, several libraries stand out in terms of functionalities for neuroscience applications. The aggregation and discussion of this information can help the neuroscience community to develop their research projects more efficiently and quickly, both by means of readily available tools and by knowing which modules may be improved, connected, or added.

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Volume Segmantics: A Python Package for Semantic Segmentation of Volumetric Data Using Pre-trained PyTorch Deep Learning Models

Cited by 2 publications

References 8 publications

Charting the molecular landscape of neuronal organisation within the hippocampus using cryo electron tomography

Charting the molecular landscape of neuronal organisation within the hippocampus using cryo electron tomography

An Overview of Open Source Deep Learning-Based Libraries for Neuroscience

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