ABSTRACT With the growth of medical data stored as bases for researches and diagnosis tasks, healthcare providers are in need of automatic processing methods to make accurate and fast image analysis such as segmentation or restoration. Most of the existing solutions to deal with these tasks are based on Deep Learning methods that require the use of powerful dedicated hardware to be executed and address a power consumption problem that is not compatible with the aforementioned requests. There is thus a demand in the development of low-cost image analysis systems with increased performances. In this work, we address this problem by proposing a fully-automatic brain tumor segmentation method based on a Convolutional Neural Network, executed by a low-cost, Deep Learning ready GPU embedded platform. We validated our approach using the BRaTS 2015 dataset to segment brain tumors and proved that an artificial neural network can be trained and used in the medical field with limited resources by redefining some of its inner operations.

中文翻译：

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将医学图像分析转移到 GPU 嵌入式系统：在脑肿瘤分割中的应用

摘要 随着作为研究和诊断任务基础存储的医疗数据的增长，医疗保健提供者需要自动处理方法来进行准确和快速的图像分析，例如分割或恢复。处理这些任务的大多数现有解决方案都基于深度学习方法，这些方法需要使用强大的专用硬件来执行并解决与上述请求不兼容的功耗问题。因此，需要开发具有更高性能的低成本图像分析系统。在这项工作中，我们通过提出一种基于卷积神经网络的全自动脑肿瘤分割方法来解决这个问题，该方法由低成本、支持深度学习的 GPU 嵌入式平台执行。