基于低资源3D U-Net的医学图像分析深度学习模型。

Girija Chetty, Mohammad Yamin, Matthew White
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引用次数: 20

摘要

深度学习是人工智能技术在图像分析和计算机领域的一个子领域,它的成功可以用来为临床放射设置建立更好的决策支持系统。使用深度学习和人工智能来检测和分割大脑区域的肿瘤组织就是这样一个场景,放射科医生可以从基于计算机的第二意见或决策支持中受益,以检测疾病的严重程度,并通过准确和及时的临床诊断来挽救患者的生命。胶质瘤是一种侵袭性脑肿瘤,形状不规则,边界模糊,是最难检测的肿瘤之一,通常需要综合分析不同类型的放射扫描才能准确检测。在本文中,我们提出了一种全自动深度学习方法,用于多模态多对比磁共振图像扫描的脑肿瘤分割。该方法基于轻量级UNET架构,由基于多模态CNN编码器-解码器的计算模型组成。利用医学图像计算和计算机辅助干预(MICCAI)协会提供的公开可用的2018年脑肿瘤分割(BraTS)挑战数据集,我们的新方法基于提出的轻量级UNet模型,不需要数据增强要求,也不使用大量计算资源,从而提高了性能。与之前的挑战任务模型相比,该模型使用了大量的计算架构和资源,并使用了不同的数据增强方法。这使得本工作中提出的模型更适用于偏远、极端和资源匮乏的卫生保健环境。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A low resource 3D U-Net based deep learning model for medical image analysis.

A low resource 3D U-Net based deep learning model for medical image analysis.

A low resource 3D U-Net based deep learning model for medical image analysis.

A low resource 3D U-Net based deep learning model for medical image analysis.

The success of deep learning, a subfield of Artificial Intelligence technologies in the field of image analysis and computer can be leveraged for building better decision support systems for clinical radiological settings. Detecting and segmenting tumorous tissues in brain region using deep learning and artificial intelligence is one such scenario, where radiologists can benefit from the computer based second opinion or decision support, for detecting the severity of disease, and survival of the subject with an accurate and timely clinical diagnosis. Gliomas are the aggressive form of brain tumors having irregular shape and ambiguous boundaries, making them one of the hardest tumors to detect, and often require a combined analysis of different types of radiological scans to make an accurate detection. In this paper, we present a fully automatic deep learning method for brain tumor segmentation in multimodal multi-contrast magnetic resonance image scans. The proposed approach is based on light weight UNET architecture, consisting of a multimodal CNN encoder-decoder based computational model. Using the publicly available Brain Tumor Segmentation (BraTS) Challenge 2018 dataset, available from the Medical Image Computing and Computer Assisted Intervention (MICCAI) society, our novel approach based on proposed light-weight UNet model, with no data augmentation requirements and without use of heavy computational resources, has resulted in an improved performance, as compared to the previous models in the challenge task that used heavy computational architectures and resources and with different data augmentation approaches. This makes the model proposed in this work more suitable for remote, extreme and low resource health care settings.

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