Image evaluation of skeletal muscle biopsies is a procedure essential to research and clinical practice. Although widely used, several major limitations exist with respect to current muscle image morphometric measurement, archiving, visualization, querying, searching, retrieval, and mining procedures: 1) Although traditional morphometric parameters, such as cross-sectional area (CSA) and minimum Feret diameter, etc., serve as critical indicators for assessing muscle function, current measurements are still largely based on manual or semi-automated methods, leading to significant labor costs with large potential inter-observer variability. 2) The current archiving of muscle images is still mainy based on outdated tools such as Excel spreadsheets and computer file folders. Given a new muscle image, it is almost impossible to quickly cross-compare, visualize, query, search, and retrieve previous cases exhibiting similar image contents with comparable morphometric measures, for the purpose of either discovering novel biological co-correlations at benchside, or providing personalized diagnosis and prognosis at bedside. 3) Although a typical muscle image often contains millions of data points (pixels), in clinical practice, doctors often condense this rich information into one or two diagnostic labels and discard the rest. Novel image markers, which are not always apparent through visual inspections or not manually quantifiable, but potentially represent critical diagnostic and prognostic values for precision medicine, have not been rigorously examined. Similarly, in basic science research, only a very limited number of known measures (e.g., CSA) are considered. Some non-traditional measures, such as myofiber shapes that hold the potential to serve as new indicators of muscle functions, are not fully investigated. 4) Current muscle image analysis and searching functions are fairly low throughput. As a frontier research area, Cloud computing can handle big image data in a distributed manner by providing high-throughput computational power. However, its application to muscle images has never been explored. MuscleMiner will provide a complete suite of tools for automated image morphometric measurements, archiving, visualization, querying, searching, content-based image retrieval, bioinformatics image mining, and Cloud computing. The objectives of this proposal are to: 1) Develop the automated morphometric measurement unit, content-based image retrieval (CBIR) unit, and the image archiving and visualization unit. 2) Develop the advanced bioinformatics image mining unit to assist in the rapid discovery and validation of new image markers. 3) Develop the Cloud computing unit to enable big image data processing and searching functions. Disseminate this freely available, Cloud-enabled imaging informatics system to muscle research community.
We propose to develop and disseminate an advanced Cloud-enabled imaging informatics tool - MuscleMiner. MuscleMiner will provide a complete suite of tools for automated image morphometric measurements, archiving, visualization, querying, searching, content-based image retrieval, and bioinformatics image mining. The goal of MuscleMiner is to offer a freely available and powerful tool to help all clinician and basic scienc muscle researchers in their daily work. Beyond fast, objective, reproducible, and automated morphometric measurements, the impact of MuscleMiner will be multiplied by laboratories using the CBIR and visualization units (Aim 1), bioinformatics image mining unit (Aim 2), and Cloud-computing unit (Aim 3) to deeply mine and fully utilize the rich information embedded in large collections of muscle images.
| Xie, Yuanpu; Xing, Fuyong; Shi, Xiaoshuang et al. (2018) Efficient and robust cell detection: A structured regression approach. Med Image Anal 44:245-254 |
| Sapkota, Manish; Liu, Fujun; Xie, Yuanpu et al. (2018) AIIMDs: An Integrated Framework of Automatic Idiopathic Inflammatory Myopathy Diagnosis for Muscle. IEEE J Biomed Health Inform 22:942-954 |
| Xing, Fuyong; Xie, Yuanpu; Yang, Lin (2016) An Automatic Learning-Based Framework for Robust Nucleus Segmentation. IEEE Trans Med Imaging 35:550-66 |
| Xing, Fuyong; Yang, Lin (2016) Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review. IEEE Rev Biomed Eng 9:234-63 |
| Zhang, Zizhao; Xing, Fuyong; Shi, Xiaoshuang et al. (2016) SemiContour: A Semi-supervised Learning Approach for Contour Detection. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2016:251-259 |
| Cai, Jinzheng; Lu, Le; Zhang, Zizhao et al. (2016) Pancreas Segmentation in MRI using Graph-Based Decision Fusion on Convolutional Neural Networks. Med Image Comput Comput Assist Interv 9901:442-450 |
| Xing, Fuyong; Shi, Xiaoshuang; Zhang, Zizhao et al. (2016) Transfer Shape Modeling Towards High-throughput Microscopy Image Segmentation. Med Image Comput Comput Assist Interv 9902:183-190 |
| Shi, Xiaoshuang; Guo, Zhenhua; Nie, Feiping et al. (2016) Two-Dimensional Whitening Reconstruction for Enhancing Robustness of Principal Component Analysis. IEEE Trans Pattern Anal Mach Intell 38:2130-6 |
| Xie, Yuanpu; Zhang, Zizhao; Sapkota, Manish et al. (2016) Spatial Clockwork Recurrent Neural Network for Muscle Perimysium Segmentation. Med Image Comput Comput Assist Interv 9901:185-193 |
| Su, Hai; Xing, Fuyong; Yang, Lin (2016) Robust Cell Detection of Histopathological Brain Tumor Images Using Sparse Reconstruction and Adaptive Dictionary Selection. IEEE Trans Med Imaging 35:1575-86 |
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