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Additional Resources for this Dataset

• The Python code for the deep-learning models, and for 3D glandular segmentations based on synthetic-CK8 datasets, are available on GitHub at https://github.com/WeisiX/ITAS3D.

Detailed Description

Our 3D imaging method is entirely slide-free and non-destructive.  We image intact tissue specimens that have been fluorescently labeled and optically cleared (to make them transparent to light).  We do not use any commercial scanners since the imaging is all done with our custom-developed in-house open-top light-sheet (OTLS) microscopes.  

Our OTLS microscopes use an sCMOS camera to collect images of optically cleared tissues in a slice-by-slice manner as we translate the specimens through an illumination “light sheet.”  The sCMOS cameras generate raw 16-bit TIFF files (grayscale) that are assembled into volumetric imaging “tiles” within the RAM of the acquisition computer.  These imaging tiles are like volumetric “bricks” of data, as shown in Fig. 1G of this paper in Nature Communications.  Each tile is then saved to the hard drive in the form of a multi-resolution HDF5 file.  As adjacent imaging tiles are scanned within the specimen, they are appended into the same HDF5 in the hard drive.

The HDF5 file is basically like a 3D TIFF stack except it contains multiple “downsampled” versions of the dataset so that users can quickly visualize or access the 3D data at whatever resolution they need.  The HDF5 file is also “chunked” so that smaller volumetric regions of interest can be quickly accessed.  The HDF5 file has an associated XML file that contains microscope metadata (e.g. stage coordinates for each tile, acquisition parameters, etc.). For our project, we also used a lossless compression method, called B3D compression, to make the HDF5 files ~10X smaller than the raw TIFF stacks in RAM.  We do not save the raw TIFF stacks.  Therefore, the B3D-compressed HDF5 file is the “rawest” form of our imaging data.

Before performing any sort of computational analysis on the imaging data, we also need to “fuse” the 3D imaging data contained in the raw HDF5 file.  This is done with an open-source program called “BigStitcher” that finely aligns all of the volumetric imaging tiles, shears the dataset, and blends the edges of those imaging tiles such that a “seamless” 3D image is generated.  In this project, we also downsample the imaging data by a factor of 2X in all three dimensions (8X-reduced dataset size) during this fusion process.  The fused dataset is saved as a TIFF stack so that we can analyze the datasets with our Python-based codes.  

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Other Publications Using This Data

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Version 1 (Current): Updated 2023/03/07