Hippocampal Segmentation Editing
Hippocampal Segmentation Editing - Related Videos
02:23
RNA Editing
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06:53
Use of Primary Cultured Hippocampal Neurons to Study the Assembly of Axon Initial Segments
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Cited by 1 •
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Here, we described a protocol to quantitatively study the assembly and structure of the axon initial segments (AIS) of hippocampal neurons that lack pre-assembled AIS due to the absence of a giant ankyrin-G.
08:20
Organotypic Hippocampal Slice Cultures
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Cited by 52 •
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We describe a method to prepare organotypic hippocampal slices that can be easily adapted to other brain regions. Brain slices are laid on porous membranes and culture media is allowed to form an interface. This method preserves the gross architecture of the hippocampus for up to 2 weeks in culture.
FAQs
Anatomical landmarks anchor each boundary decision to visible brain structure rather than to the initial label alone. During Hippocampal Segmentation Editing, they help identify whether a labeled voxel belongs to the hippocampus or neighboring tissue and whether the boundary remains anatomically plausible from slice to slice. This reduces arbitrary edits and supports more consistent volume and shape estimates.
Reviewing adjacent MRI planes helps preserve a coherent three-dimensional label. A boundary that seems reasonable in one slice may become inconsistent when neighboring slices are examined, revealing abrupt changes or misplaced tissue labels. Comparing planes therefore provides a practical check on continuity and anatomical consistency, improving confidence that the final segmentation represents the hippocampus across the imaged region.
Correcting both included neighboring tissue and missed hippocampal regions matters because either error can distort the measured boundary. Extra tissue can make the labeled structure extend beyond the hippocampus, whereas omissions can leave portions unrepresented. Addressing both types of error produces more trustworthy estimates of hippocampal volume and shape for downstream analysis.
The workflow starts with inspection of MRI slices and their voxel labels. The reviewer refines boundaries where neighboring tissue has been included or hippocampal tissue has been missed, using anatomical landmarks and comparisons across adjacent planes. Quality control then examines the edited result for consistency, so the resulting labels can support dependable measurements and reproducible longitudinal analyses.
These measurements support studies of hippocampal development and aging, as well as investigations of neurodegenerative disease, psychiatric conditions, and memory. Because editing improves the reliability of volume and shape estimates, it gives neuroscience researchers a stronger structural measure to relate to the question being studied, while keeping interpretation tied to the quality of the underlying segmentation.
Edited labels can provide a carefully checked basis for judging whether an automated segmentation has included neighboring tissue or missed hippocampal regions. Comparing automated outputs with refined boundaries helps identify where the method needs evaluation, while quality control documents the reliability of the labels used in that assessment. This makes editing relevant both to method validation and reproducible analysis.