Abstract
Cryo-imaging sections and images a whole mouse and provides ~ 120-GBytes of microscopic 3D color anatomy and fluorescence images, making fully manual analysis of metastases an onerous task. A convolutional neural network (CNN)-based metastases segmentation algorithm included three steps: candidate segmentation, candidate classification, and semi-automatic correction of the classification result. The candidate segmentation generated > 5000 candidates in each of the breast cancer-bearing mice. Random forest classifier with multi-scale CNN features and hand-crafted intensity and morphology features achieved 0.8645 ± 0.0858, 0.9738 ± 0.0074, and 0.9709 ± 0.0182 sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic (ROC), with fourfold cross validation. Classification results guided manual correction by an expert with our in-house MATLAB software. Finally, 225, 148, 165, and 344 metastases were identified in the four cancer mice. With CNN-based segmentation, the human intervention time was reduced from > 12 to ~ 2 h. We demonstrated that 4T1 breast cancer metastases spread to the lung, liver, bone, and brain. Assessing the size and distribution of metastases proves the usefulness and robustness of cryo-imaging and our software for evaluating new cancer imaging and therapeutics technologies. Application of the method with only minor modification to a pancreatic metastatic cancer model demonstrated generalizability to other tumor models.
Highlights
Cryo-imaging sections and images a whole mouse and provides ~ 120-GBytes of microscopic 3D color anatomy and fluorescence images, making fully manual analysis of metastases an onerous task
We developed a MATLAB graphical user interface (GUI) for easy and fast semi-automatic exclusion of false positive (FP) candidates and used Amira software to include false negative (FN) candidates at the same time
We analyze the performance of candidate segmentation and classification, and quantify the size and distribution of metastases
Summary
Cryo-imaging sections and images a whole mouse and provides ~ 120-GBytes of microscopic 3D color anatomy and fluorescence images, making fully manual analysis of metastases an onerous task. Preclinical mouse models for metastatic breast cancer include tail vein injection, orthotopic, and intra-cardiac models[4], which induce metastases at different locations. One of the major functionalities of cryo-imaging is to provide the ground-truth for the identification of metastatic tumors using fluorescent-protein-labeled cancer cells. High-resolution cryo-imaging of a whole mouse in color and fluorescence images could be as large as 120 GB and, manual analysis is time consuming. Fully convolutional neural networks (FCN)-based methods have been applied in various major tumor segmentation tasks, such as breast tumors in mammograms[24], brain tumors in MRI25, hepatic tumors in CT26, and pancreatic tumors in CT27. Three-dimensional networks capture the volumetric information but are computationally more expensive
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