We are creating a cancer imaging and therapy analysis platform (CITAP), featuring image analysis/visualization software and multi-spectral cryo-imaging to support innovations in preclinical cancer research. Cryo-imaging repeatedly sections and tiles microscope images of the tissue block face, providing color anatomy and molecular fluorescence 3D microscopic imaging over vast volumes as large as a whole mouse, with single-metastatic-cell sensitivity. We utilized DenseVNet from NiftyNet for multi-organ segmentation on color anatomy images to further analyze major organs. The proposed algorithm was trained/validated/tested on 70/5/4 color anatomy volumes with manually labeled lung, liver, and spleen. The mean Dice similarity coefficient for lung, liver, and spleen in the test set were 0.89±0.01, 0.92±0.01, and 0.83±0.04. We deem Dice coefficient of <0.9 good for analyzing distribution of metastases. To segment GFP-labeled breast cancer metastases in high resolution green fluorescence images, big and small candidates were segmented using marker-based watershed and multi-scale Laplacian of Gaussian filtering followed by Otsu segmentation respectively. A bounding box around each candidate was classified with a 3D convolutional neural network (CNN). In one test mouse with 226 metastases, CNNbased classification and random forest with hand-crafted features achieved sensitivity/specificity of 0.95/0.89 and 0.92/0.82, respectively. DenseVNet-based organ segmentation allows automatic quantification of GFP-labeled metastases in each organ of interest. In the test mouse with 226 metastases, 78 (1 with size <2mm, 21 with size 0.5mm-2mm, and 56 with size <0.5mm) and 24 (1 with size <2mm, 11 with size 0.5-2mm, and 12 with size <0.5mm) were found in the lung and liver respectively.
KEYWORDS: Tumors, Magnetic resonance imaging, Image segmentation, Signal detection, Signal to noise ratio, Image registration, Green fluorescent protein, 3D image processing, Cancer
We created a cancer imaging and therapy platform (CITP) consisting of software and multi-spectral cryo-imaging to support innovations in preclinical cancer research. Cryo-imaging repeatedly sections and tiles microscope images of the tissue block face, providing anatomical episcopic color and molecular fluorescence, enabling 3D microscopic imaging of the entire mouse with single metastatic cell sensitivity. Our platform allows tumor molecular imaging validation with MRI and cryo images registration, GFP metastatic tumor segmentation and quantitative analysis, all of which are important processes in the CITP visualization/analysis pipeline. Our standard approach to register MRI to the cryo color volume involves preprocess Æ affine Æ B-spline non-rigid 3D mutual information registration. We further developed modified mask registration to allow improved registration quality within the created 3D cuboid mask on the organ of interest. In 3 mice kidneys, standard and mask registration yields Dice index of 84% ± 2% and 90% ± 2%, respectively. To segment big metastases in GFP, we use marker based watershed with intensity thresholding. For small metastases, we apply Laplacian of Gaussian filtering to get candidate metastases and use morphological features and support vector machine to classify the candidates. In a test mouse, sensitivity/specificity for metastases detection was 94.1%/99.82% as compared with manual segmentation of 202 metastases. Quantitative analysis of molecular MR imaging agent CREKA-Gd using Rose SNR in the lung of a test mouse showed that all micro-metastases ≥ 0.25 mm2 were detectable with Rose SNR ≥ 4 and around 36% of micro-metastases < 0.25 mm2 were detectable.
KEYWORDS: Image registration, Magnetic resonance imaging, Imaging systems, 3D image processing, Luminescence, 3D acquisition, Tumors, Green fluorescent protein, Image resolution, Visualization, Cancer
We created a metastasis imaging, analysis platform consisting of software and multi-spectral cryo-imaging system suitable for evaluating emerging imaging agents targeting micro-metastatic tumor. We analyzed CREKA-Gd in MRI, followed by cryo-imaging which repeatedly sectioned and tiled microscope images of the tissue block face, providing anatomical bright field and molecular fluorescence, enabling 3D microscopic imaging of the entire mouse with single metastatic cell sensitivity. To register MRI volumes to the cryo bright field reference, we used our standard mutual information, non-rigid registration which proceeded: preprocess → affine → B-spline non-rigid 3D registration. In this report, we created two modified approaches: mask where we registered locally over a smaller rectangular solid, and sliding organ. Briefly, in sliding organ, we segmented the organ, registered the organ and body volumes separately and combined results. Though sliding organ required manual annotation, it provided the best result as a standard to measure other registration methods. Regularization parameters for standard and mask methods were optimized in a grid search. Evaluations consisted of DICE, and visual scoring of a checkerboard display. Standard had accuracy of 2 voxels in all regions except near the kidney, where there were 5 voxels sliding. After mask and sliding organ correction, kidneys sliding were within 2 voxels, and Dice overlap increased 4%–10% in mask compared to standard. Mask generated comparable results with sliding organ and allowed a semi-automatic process.
We are developing enhanced volume rendering techniques for color image data. One target application is cryo-imaging,
which provides whole-mouse, micron-scale, anatomical color, and molecular fluorescence image volumes by
alternatively sectioning and imaging the frozen tissue block face. With the rich color images provided by cryo-imaging,
we use true-color volume rendering and visually enhance anatomical regions by proper selection of voxel opacity. To
compute opacity, we use color and/or gradient feature detection followed by suitable opacity transfer functions (OTF).
An interactive user interface allows one to select from among multiple color and gradient feature detectors, OTF's, and
their associated parameters, and to compute in live time new volume visualizations from within the Amira platform. We
are also developing multi-resolution volume rendering techniques to accommodate extremely large (⪆60GB) cryo-image
data sets. Together, these enhancements enable us to interactively interrogate cryo-image volume data and create useful
renderings with "implicit segmentation" of organs.
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