A semi-automatic validation tool for whole mouse metastatic tumor molecular imaging using the cryo-imaging cancer imaging and therapy platform (CITP)

Yiqiao Liu; Madhu Gargesha; Mohammed Qutaish; Zhuxian Zhou; Zhengrong Lu; David Wilson

doi:10.1117/12.2293803

12 March 2018 A semi-automatic validation tool for whole mouse metastatic tumor molecular imaging using the cryo-imaging cancer imaging and therapy platform (CITP)

Yiqiao Liu, Madhu Gargesha, Mohammed Qutaish, Zhuxian Zhou, Zhengrong Lu, David Wilson

Author Affiliations +

Proceedings Volume 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging; 105781B (2018) https://doi.org/10.1117/12.2293803
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

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.

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Yiqiao Liu, Madhu Gargesha, Mohammed Qutaish, Zhuxian Zhou, Zhengrong Lu, and David Wilson "A semi-automatic validation tool for whole mouse metastatic tumor molecular imaging using the cryo-imaging cancer imaging and therapy platform (CITP)", Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105781B (12 March 2018); https://doi.org/10.1117/12.2293803

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KEYWORDS

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Magnetic resonance imaging

Image segmentation

Signal detection

Signal to noise ratio

Image registration

Green fluorescent protein

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