Dataset Information

A Method for 3D Histopathology Reconstruction Supporting Mouse Microvasculature Analysis.

ABSTRACT: Structural abnormalities of the microvasculature can impair perfusion and function. Conventional histology provides good spatial resolution with which to evaluate the microvascular structure but affords no 3-dimensional information; this limitation could lead to misinterpretations of the complex microvessel network in health and disease. The objective of this study was to develop and evaluate an accurate, fully automated 3D histology reconstruction method to visualize the arterioles and venules within the mouse hind-limb. Sections of the tibialis anterior muscle from C57BL/J6 mice (both normal and subjected to femoral artery excision) were reconstructed using pairwise rigid and affine registrations of 5 µm-thick, paraffin-embedded serial sections digitized at 0.25 µm/pixel. Low-resolution intensity-based rigid registration was used to initialize the nucleus landmark-based registration, and conventional high-resolution intensity-based registration method. The affine nucleus landmark-based registration was developed in this work and was compared to the conventional affine high-resolution intensity-based registration method. Target registration errors were measured between adjacent tissue sections (pairwise error), as well as with respect to a 3D reference reconstruction (accumulated error, to capture propagation of error through the stack of sections). Accumulated error measures were lower (p < 0.01) for the nucleus landmark technique and superior vasculature continuity was observed. These findings indicate that registration based on automatic extraction and correspondence of small, homologous landmarks may support accurate 3D histology reconstruction. This technique avoids the otherwise problematic "banana-into-cylinder" effect observed using conventional methods that optimize the pairwise alignment of salient structures, forcing them to be section-orthogonal. This approach will provide a valuable tool for high-accuracy 3D histology tissue reconstructions for analysis of diseased microvasculature.

SUBMITTER: Xu Y

PROVIDER: S-EPMC4449209 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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A Method for 3D Histopathology Reconstruction Supporting Mouse Microvasculature Analysis.

Xu Yiwen Y Pickering J Geoffrey JG Nong Zengxuan Z Gibson Eli E Arpino John-Michael JM Yin Hao H Ward Aaron D AD

PloS one 20150529 5

Structural abnormalities of the microvasculature can impair perfusion and function. Conventional histology provides good spatial resolution with which to evaluate the microvascular structure but affords no 3-dimensional information; this limitation could lead to misinterpretations of the complex microvessel network in health and disease. The objective of this study was to develop and evaluate an accurate, fully automated 3D histology reconstruction method to visualize the arterioles and venules ...[more]

PMID: 26024221

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Project description:The use of MRI for prostate cancer diagnosis and treatment is increasing rapidly. However, identifying the presence and extent of cancer on MRI remains challenging, leading to high variability in detection even among expert radiologists. Improvement in cancer detection on MRI is essential to reducing this variability and maximizing the clinical utility of MRI. To date, such improvement has been limited by the lack of accurately labeled MRI datasets. Data from patients who underwent radical prostatectomy enables the spatial alignment of digitized histopathology images of the resected prostate with corresponding pre-surgical MRI. This alignment facilitates the delineation of detailed cancer labels on MRI via the projection of cancer from histopathology images onto MRI. We introduce a framework that performs 3D registration of whole-mount histopathology images to pre-surgical MRI in three steps. First, we developed a novel multi-image super-resolution generative adversarial network (miSRGAN), which learns information useful for 3D registration by producing a reconstructed 3D MRI. Second, we trained the network to learn information between histopathology slices to facilitate the application of 3D registration methods. Third, we registered the reconstructed 3D histopathology volumes to the reconstructed 3D MRI, mapping the extent of cancer from histopathology images onto MRI without the need for slice-to-slice correspondence. When compared to interpolation methods, our super-resolution reconstruction resulted in the highest PSNR relative to clinical 3D MRI (32.15 dB vs 30.16 dB for BSpline interpolation). Moreover, the registration of 3D volumes reconstructed via super-resolution for both MRI and histopathology images showed the best alignment of cancer regions when compared to (1) the state-of-the-art RAPSODI approach, (2) volumes that were not reconstructed, or (3) volumes that were reconstructed using nearest neighbor, linear, or BSpline interpolations. The improved 3D alignment of histopathology images and MRI facilitates the projection of accurate cancer labels on MRI, allowing for the development of improved MRI interpretation schemes and machine learning models to automatically detect cancer on MRI.

Project description:BackgroundThe microvasculature is the network of blood vessels involved in delivering nutrients and gases necessary for tissue survival. Study of the microvasculature often involves immunohistological methods. While useful for visualizing microvasculature at the µm scale in specific regions of interest, immunohistology is not well suited to visualize the global microvascular architecture in an organ. Hence, use of immunohistology precludes visualization of the entire microvasculature of an organ, and thus impedes study of global changes in the microvasculature that occur in concert with changes in tissue due to various disease states. Therefore, there is a critical need for a simple, relatively rapid technique that will facilitate visualization of the microvascular network of an entire tissue.Methodology/principal findingsThe systemic vasculature of a mouse is stained with the fluorescent lipophilic dye DiI using a method called "vessel painting". The brain, or other organ of interest, is harvested and fixed in 4% paraformaldehyde. The organ is then sliced into 1 mm sections and optically cleared, or made transparent, using FocusClear, a proprietary optical clearing agent. After optical clearing, the DiI-labeled tissue microvasculature is imaged using confocal fluorescence microscopy and adjacent image stacks tiled together to produce a depth-encoded map of the microvasculature in the tissue slice. We demonstrated that the use of optical clearing enhances both the tissue imaging depth and the estimate of the vascular density. Using our "optical histology" technique, we visualized microvasculature in the mouse brain to a depth of 850 µm.Conclusions/significancePresented here are maps of the microvasculature in 1 mm thick slices of mouse brain. Using combined optical clearing and optical imaging techniques, we devised a methodology to enhance the visualization of the microvasculature in thick tissues. We believe this technique could potentially be used to generate a three-dimensional map of the microvasculature in an entire organ.

Dataset Information

A Method for 3D Histopathology Reconstruction Supporting Mouse Microvasculature Analysis.

Publications

A Method for 3D Histopathology Reconstruction Supporting Mouse Microvasculature Analysis.

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