论文标题
脑出血分段的图形方法
A Graphical Approach For Brain Haemorrhage Segmentation
论文作者
论文摘要
大脑的出血是15至24岁的人死亡的主要原因，也是年龄较大的人的第三大死亡原因。计算机断层扫描（CT）是一种成像方式，用于诊断神经系统紧急情况，包括中风和创伤性脑损伤。深度学习和图像处理的最新进展采用了不同的模式，例如CT扫描，以帮助自动化脑出血发生的检测和分割。在本文中，我们提出了一种由传统卷积神经网络（CNN）以及图形神经网络（GNN）组成的建筑的新颖实现，以生成一个整体模型，以实现大脑出血分段的任务。GNN在邻里聚集原则上起作用，从而提供了图像中全球结构的可靠估计。 GNN的工作方式很少，因此需要更少的参数来使用。我们能够在实施情况下获得有限的数据骰子系数分数约为0.81。
Haemorrhaging of the brain is the leading cause of death in people between the ages of 15 and 24 and the third leading cause of death in people older than that. Computed tomography (CT) is an imaging modality used to diagnose neurological emergencies, including stroke and traumatic brain injury. Recent advances in Deep Learning and Image Processing have utilised different modalities like CT scans to help automate the detection and segmentation of brain haemorrhage occurrences. In this paper, we propose a novel implementation of an architecture consisting of traditional Convolutional Neural Networks(CNN) along with Graph Neural Networks(GNN) to produce a holistic model for the task of brain haemorrhage segmentation.GNNs work on the principle of neighbourhood aggregation thus providing a reliable estimate of global structures present in images. GNNs work with few layers thus in turn requiring fewer parameters to work with. We were able to achieve a dice coefficient score of around 0.81 with limited data with our implementation.
