论文标题
用基于CNN的Poisson回归对斑块中的脑病变体积进行建模
Modelling brain lesion volume in patches with CNN-based Poisson Regression
论文作者
论文摘要
监测病变的进展对于临床反应很重要。诸如病变量之类的摘要统计数据是客观且易于解释的，这可以帮助临床医生评估病变的生长或衰减。 CNN通常在医学图像分割中使用，其能力在较大的环境中产生有用的功能及其相关的有效迭代基训练的能力。许多CNN架构需要数十万个参数才能产生良好的分割。在这项工作中，实施了有效的计算廉价的CNN，以估计磁共振（MR）图像中预定义斑块大小中病变体素的数量。 CNN的输出被解释为贴片上的条件泊松参数，从而允许使用标准的小批量梯度下降。 ISLE2015（SISS）数据用于训练和评估该模型，该模型通过估算原始特征的病变体积，准确地识别出较大病变体积的病变图像，其中86％的配对样品贴片中有86％。开发和使用估计病变量以帮助进行分割的模型选择的论点。
Monitoring the progression of lesions is important for clinical response. Summary statistics such as lesion volume are objective and easy to interpret, which can help clinicians assess lesion growth or decay. CNNs are commonly used in medical image segmentation for their ability to produce useful features within large contexts and their associated efficient iterative patch-based training. Many CNN architectures require hundreds of thousands parameters to yield a good segmentation. In this work, an efficient, computationally inexpensive CNN is implemented to estimate the number of lesion voxels in a predefined patch size from magnetic resonance (MR) images. The output of the CNN is interpreted as the conditional Poisson parameter over the patch, allowing standard mini-batch gradient descent to be employed. The ISLES2015 (SISS) data is used to train and evaluate the model, which by estimating lesion volume from raw features, accurately identified the lesion image with the larger lesion volume for 86% of paired sample patches. An argument for the development and use of estimating lesion volumes to also aid in model selection for segmentation is made.
