Manual segmentations of the 6 cortical layers were created on 51 regions of the cortex, distributed across 13 of the original histological BigBrain sections rescanned at a higher in-plan resolution of 5 μm (Fig 2 ). These regions were chosen to give a distribution of examples demonstrating a variety of cytoarchitectures, a variety of rostrocaudal locations, in both gyri and sulci, from sections where the cortex was sectioned tangentially.
Layers were segmented according to the following criteria. Layer I, the molecular layer, is relatively cell sparse with few neurons and glia. Layer II, the external granular layer, is a much denser band of small granular cells. Layer III, the external pyramidal layer, is characterized by large pyramidal neurons that become more densely packed toward its lower extent. Layer IV, the internal granular layer (usually referred to simply as the “granular layer”), generally contains only granular neurons, bounded at its lower extent by pyramidal neurons of layer V. Layer V, the internal pyramidal layer, contains large but relatively sparse pyramidal neurons, whereas layer VI, the multiform layer, has a lower density of pyramidal neurons [1 ]. Alongside association areas with such typical neocortical laminar structure, samples from the primary visual and motor cortices were specifically included as they exhibit unique laminar characteristics.
Segmentations were verified by expert anatomists: SB, NPG, and KZ. This resolution is sufficient to distinguish individual cell bodies, a prerequisite to analyze their distribution pattern in cortical layers and to delineate the layers. Averaged across all training examples, layer classes contributed to profiles as follows: background/cerebrospinal fluid (CSF): 14.6%, layer I: 7.5%, layer II: 5.6%, layer III: 20.8%, layer IV: 5.5%, layer V: 14.8%, layer VI: 17.8%, white matter: 13.4%. For the cortical layers, these values represent an approximate relative thickness.
Manual segmentations were then coregistered to the full aligned 3D BigBrain space. The manually drawn layers were used to create corresponding pial and white surfaces. These cortical boundaries were extended beyond layer VI and beyond the pial surface between 0.25 mm and 0.75 mm so as to match the variability of cortical extent in the test profile data set. Training profiles were created by sampling raw, smoothed, and manually segmented data, generating thousands of profiles per sample. Each pixel in the labeled data had a class value of 0 to 7, in which pixels superficial to the pial surface were set to 0, followed by layers numbered 1 to 6, and white matter was classed as 7. This 1D profile-based approach greatly expanded the training data set from 51 labeled 2D samples to over 500,000 profiles. Coregistered manually annotated data are available to download atftp://bigbrain.loris.ca/BigBrainRelease.2015/Layer_Segmentation/Manual_Annotations/ .
Layers were segmented according to the following criteria. Layer I, the molecular layer, is relatively cell sparse with few neurons and glia. Layer II, the external granular layer, is a much denser band of small granular cells. Layer III, the external pyramidal layer, is characterized by large pyramidal neurons that become more densely packed toward its lower extent. Layer IV, the internal granular layer (usually referred to simply as the “granular layer”), generally contains only granular neurons, bounded at its lower extent by pyramidal neurons of layer V. Layer V, the internal pyramidal layer, contains large but relatively sparse pyramidal neurons, whereas layer VI, the multiform layer, has a lower density of pyramidal neurons [1 ]. Alongside association areas with such typical neocortical laminar structure, samples from the primary visual and motor cortices were specifically included as they exhibit unique laminar characteristics.
Segmentations were verified by expert anatomists: SB, NPG, and KZ. This resolution is sufficient to distinguish individual cell bodies, a prerequisite to analyze their distribution pattern in cortical layers and to delineate the layers. Averaged across all training examples, layer classes contributed to profiles as follows: background/cerebrospinal fluid (CSF): 14.6%, layer I: 7.5%, layer II: 5.6%, layer III: 20.8%, layer IV: 5.5%, layer V: 14.8%, layer VI: 17.8%, white matter: 13.4%. For the cortical layers, these values represent an approximate relative thickness.
Manual segmentations were then coregistered to the full aligned 3D BigBrain space. The manually drawn layers were used to create corresponding pial and white surfaces. These cortical boundaries were extended beyond layer VI and beyond the pial surface between 0.25 mm and 0.75 mm so as to match the variability of cortical extent in the test profile data set. Training profiles were created by sampling raw, smoothed, and manually segmented data, generating thousands of profiles per sample. Each pixel in the labeled data had a class value of 0 to 7, in which pixels superficial to the pial surface were set to 0, followed by layers numbered 1 to 6, and white matter was classed as 7. This 1D profile-based approach greatly expanded the training data set from 51 labeled 2D samples to over 500,000 profiles. Coregistered manually annotated data are available to download at
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