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Proceedings Paper

Deep learning based multi-organ segmentation and metastases segmentation in whole mouse body and the cryo-imaging cancer imaging and therapy analysis platform (CITAP)
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Paper Abstract

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.

Paper Details

Date Published: 28 February 2020
PDF: 11 pages
Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113170V (28 February 2020); doi: 10.1117/12.2549801
Show Author Affiliations
Yiqiao Liu, Case Western Reserve Univ. (United States)
Madhu Gargesha, BioInVision, Inc. (United States)
Mohammed Qutaish, Case Western Reserve Univ. (United States)
Zhuxian Zhou, Case Western Reserve Univ. (United States)
Bryan Scott, BioInVision, Inc. (United States)
Hamed Yousefi, Case Western Reserve Univ. (United States)
Zhengrong Lu, Case Western Reserve Univ. (United States)
David L. Wilson, Case Western Reserve Univ. (United States)
BioInVision, Inc. (United States)


Published in SPIE Proceedings Vol. 11317:
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor S. Gimi, Editor(s)

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