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Enqi Ma

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Proceedings Article | 1 April 2024 Paper

Deep Learning based brain segmentation and tumor radiogenomic classification

Enqi Ma, Ben Wu

Proceedings Volume 13077, 1307703 (2024) https://doi.org/10.1117/12.3027105

KEYWORDS: 3D modeling, Tumors, Education and training, Image segmentation, Brain, Performance modeling, Data modeling, Magnetic resonance imaging, Deep learning, Neuroimaging

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Brain tumors were found to have a strong indicator, the methylation status of the O6-methylguanine-Deoxyribonucleic acid methyl-transferase gene promoter. The value of this indicator suggests the stage and severity of the Tumor. To evaluate the stage of cancer a patient is in, an ensemble model is proposed. This study combined 2D and 3D Densenet, Resnet, and Efficientnet to create an ensemble model and achieved the highest result of 0.87 accuracy, 0.90 precision, 0.86 area under the receiver operating characteristic curve (AUC), and 0.80 recall. The efficiency of artificial intelligence (AI) diagnosis and its relatively high accuracy both help radiologists confirm their evaluations and ensure more safety for patients by double-checking the radiologist’s diagnosis. Brain tumor segmentation allows for precise detection of malignant tumors on radiological brain scans. Conventionally, such a process has been done by trained radiologists, and requires a significant amount of time and effort. In assistance of a specialist, this study proposed to segment cerebral magnetic resonance imaging (MRI) scans with machine learning. Using the Unet Architecture, this study achieved dice score of 0.99 ± 0.00013, an accuracy of 0.99 ± 0.000070, a recall of 0.99 ± 0.00014, and a f1 score of 0.99 ± 0.00013. Through comparing the segmentation ground truth and the model output, we see that the output mappings are largely in line with the ground truth in all categories presented in the ground truth. With computational power of 1 Graphics processing unit(GPU), it takes less than 10 seconds to build the model, read in an input and generate a segmented result. The efficiency of this process can possibly assist radiologists in the process of tumor diagnosis and may provide them the ability to give easier and less costly diagnosis, which ultimately saves time and patients’ lives.

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