NeuroShape: exploiting neural architectures for shape analysis of ultrastructural 3D neuroscience morphologies

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dc.contributor.authorShaffique, Humairaen_US
dc.contributor.authorShah, Uzairen_US
dc.contributor.authorAlzubaidi, Mahmooden_US
dc.contributor.authorSchneider, Jensen_US
dc.contributor.authorMagistretti, Pierre Juliusen_US
dc.contributor.authorCali, Corradoen_US
dc.contributor.authorHouseh, Mowafaen_US
dc.contributor.authorAgus, Marcoen_US
dc.contributor.editorComino Trinidad, Marcen_US
dc.contributor.editorMancinelli, Claudioen_US
dc.contributor.editorMaggioli, Filippoen_US
dc.contributor.editorRomanengo, Chiaraen_US
dc.contributor.editorCabiddu, Danielaen_US
dc.contributor.editorGiorgi, Danielaen_US
dc.date.accessioned2025-11-21T07:28:28Z
dc.date.available2025-11-21T07:28:28Z
dc.date.issued2025
dc.description.abstractRecent advances in volume electron microscopy (EM) enable nanometric-scale 3D reconstructions of neural tissue, providing unprecedented opportunities for studying cellular and subcellular morphology in neuroscience. The geometry of structures such as nuclei, neurites, and organelles can encode phenotypic information relevant to both functional specialization and pathological conditions, and thus represents a valuable complement to connectivity-based approaches in connectomics. While previous studies relied on handcrafted descriptors and classical machine learning for morphology analysis, recent progress in deep learning for 3D shape understanding offers new opportunities to learn robust, task-specific representations directly from geometric data. In this work we present NeuroShape, a first exploration of modern deep learning methods for shape analysis of ultrastructural 3D neuroscience morphologies. We introduce two annotated datasets derived from EM reconstructions: one of nuclei envelopes, and one of neurites and neural organelles. We benchmark two state-of-the-art neural architectures for 3D geometry (DiffusionNet [SACO22] and Laplacian2Mesh [DWL∗24]) and compare them against traditional feature-based descriptors previously used in neural morphology analysis. Our preliminary results highlight both the feasibility and the challenges of applying deep learning shape analysis techniques in this domain, and we release the datasets as a reference resource for future studies.en_US
dc.description.sectionheadersDatasets
dc.description.seriesinformationSmart Tools and Applications in Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20251333
dc.identifier.isbn978-3-03868-296-7
dc.identifier.issn2617-4855
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.2312/stag.20251333
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/stag20251333
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Shape analysis; Image segmentation; Point-based models; Mesh models; Supervised learning by classification; Neural networks; Applied computing → Computational biology
dc.subjectComputing methodologies → Shape analysis
dc.subjectImage segmentation
dc.subjectPoint
dc.subjectbased models
dc.subjectMesh models
dc.subjectSupervised learning by classification
dc.subjectNeural networks
dc.subjectApplied computing → Computational biology
dc.titleNeuroShape: exploiting neural architectures for shape analysis of ultrastructural 3D neuroscience morphologiesen_US
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Italian Chapter Conference 2025 - Smart Tools and Apps in Graphics