利用掩膜RCNN检测脑肿瘤

V. Asha, S. Sreeja, Binju Saju, Pavan Desai, K. M. Pavan, G. Kumari
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引用次数: 0

摘要

在MRI扫描报告中,肿瘤大小的测量是检测脑肿瘤治疗的关键挑战。从3D MRI扫描中手动分割脑肿瘤是一种花费医生更多时间来检测脑肿瘤的方法。在此,需要一种可靠的完全自动化的脑肿瘤分割方法来精确评估肿瘤的范围。在本研究中,它提供了一种完全自动化的方法来分割脑肿瘤,利用从各种生物医学设备接收的医学图像数据,采用各种成像方式,如x射线,CT扫描,MRI,乳房x光片等。最后,本文尝试了一种利用掩膜R-CNN模型检测脑MRI数据中肿瘤的技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Brain Tumor Detection using Mask RCNN
The measuring of tumour size is a key challenge in detecting brain tumour treatment out of MRI scan reports. Manual brain tumour segmentation from 3D MRI scans is a method where it costs more time for the doctors to detect the brain tumor. Here, a trustworthy completely automated segmentation method for brain tumour is needed for precise tumour extent assessment. In this research, it is provided with a completely automated method for segmenting brain tumours utilising medical image data received from various biomedical equipment that employs a variety of imaging modalities, such as X-rays, CT scans, MRI, mammograms, and so on. Finally here it is tried for emerging a technique which uses mask R-CNN model for detecting tumour in brain MRI data.
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