A Deep Learning Approach for Segmentation, Classification, and Visualization of 3-D High-Frequency Ultrasound Images of Mouse Embryos
| Autor: | Orlando Aristizabal, William Das, Daniel H. Turnbull, Chuiyu Wang, Jeffrey A. Ketterling, Tongda Xu, Yao Wang, Jonathan Mamou, Nitin Nair, Ziming Qiu, Jack Langerman |
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| Rok vydání: | 2021 |
| Předmět: |
Acoustics and Ultrasonics
Artificial neural network business.industry Computer science Deep learning Pattern recognition Image processing Image segmentation Convolutional neural network Article Backpropagation Mice Deep Learning Imaging Three-Dimensional Region of interest Image Processing Computer-Assisted Animals Segmentation Neural Networks Computer Artificial intelligence Electrical and Electronic Engineering business Instrumentation Ultrasonography |
| Zdroj: | IEEE Trans Ultrason Ferroelectr Freq Control |
| ISSN: | 1525-8955 0885-3010 |
| Popis: | Segmentation and mutant classification of high-frequency ultrasound (HFU) mouse embryo brain ventricle (BV) and body images can provide valuable information for developmental biologists. However, manual segmentation and identification of BV and body requires substantial time and expertise. This article proposes an accurate, efficient and explainable deep learning pipeline for automatic segmentation and classification of the BV and body. For segmentation, a two-stage framework is implemented. The first stage produces a low-resolution segmentation map, which is then used to crop a region of interest (ROI) around the target object and serve as the probability map of the autocontext input for the second-stage fine-resolution refinement network. The segmentation then becomes tractable on high-resolution 3-D images without time-consuming sliding windows. The proposed segmentation method significantly reduces inference time (102.36–0.09 s/volume $\approx 1000\times $ faster) while maintaining high accuracy comparable to previous sliding-window approaches. Based on the BV and body segmentation map, a volumetric convolutional neural network (CNN) is trained to perform a mutant classification task. Through backpropagating the gradients of the predictions to the input BV and body segmentation map, the trained classifier is found to largely focus on the region where the Engrailed-1 (En1) mutation phenotype is known to manifest itself. This suggests that gradient backpropagation of deep learning classifiers may provide a powerful tool for automatically detecting unknown phenotypes associated with a known genetic mutation. |
| Databáze: | OpenAIRE |
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