AI-Powered Radiomics Algorithm Based on Slice Pooling for the Glioma Grading
| Autor: | Lei Shi, Panpan Man, Jingliang Cheng, Jie Bai, Longfei Li, Pei Pei Wang, Guohua Zhao, Yongcai Tao, Guan Yang, Yusong Lin |
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| Rok vydání: | 2022 |
| Předmět: |
Computer science
Pooling ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION CAD Cad system Computer Science Applications ComputingMethodologies_PATTERNRECOGNITION Glioma grading Radiomics Control and Systems Engineering Automatic segmentation Segmentation Manual segmentation Electrical and Electronic Engineering Algorithm Information Systems |
| Zdroj: | IEEE Transactions on Industrial Informatics. 18:5383-5393 |
| ISSN: | 1941-0050 1551-3203 |
| DOI: | 10.1109/tii.2021.3105665 |
| Popis: | Glioma segmentation in the glioma grading computer-aided diagnosis (CAD) system requires manual delineation from radiologists, adding substantially to their workload. Although automatic segmentation is powerful, it cannot fully delegate power to artificial intelligence. We proposed an AI-powered radiomics algorithm based on slice pooling (AI-RASP). AI-RASP generated compressed images by compressing the gray value of each magnetic resonance imaging slice for radiologists to segment manually. In addition, AI-RASP integrated radiomics models to verify the glioma grading effect and the availability of compressed images . AI-RASP significantly reduced the time of manual segmentation. Results reported on multicenter datasets revealed that our architecture was better than that of the traditional manual segmentation while being over five times faster. The radiomics model with slice pooling mechanism achieved area under the curve values of 0.86, 086, and 0.83 in the validation cohorts. Radiologists and patients can benefit from a CAD system integrated with AI-RASP. |
| Databáze: | OpenAIRE |
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