论文标题
对体积MRI的前列腺分割的良好模型培训的全面研究
Comprehensive study of good model training for prostate segmentation in volumetric MRI
论文作者
论文摘要
前列腺癌是2020年国际上第三大常见的癌症，是乳腺癌和肺癌之后的。此外，近年来，前列腺癌显示出趋势的增加。根据临床经验，如果提早发现和治疗了此问题，则可能有很高的生存机会。有助于诊断前列腺癌的一项任务是磁共振成像中的前列腺分割。临床专家进行的手动分割具有其缺点，例如：观察者所需的高时间和集中度；以及观察者内和观察者内变异性。这就是为什么近年来出现了基于卷积神经网络的前列腺的自动方法。他们中的许多人都有新颖的构建结构。在本文中，我通过将它们调整为前列腺预测的任务，对几种深度学习模型进行详尽的研究。我不使用新颖的体系结构，而是将我的工作更多地关注如何训练网络。我的方法基于Resnext101 3D编码器和UNET3D解码器。我提供了一项研究，研究决议在重新采样数据中的重要性，这是其他人以前做过的。
Prostate cancer was the third most common cancer in 2020 internationally, coming after breast cancer and lung cancer. Furthermore, in recent years prostate cancer has shown an increasing trend. According to clinical experience, if this problem is detected and treated early, there can be a high chance of survival for the patient. One task that helps diagnose prostate cancer is prostate segmentation from magnetic resonance imaging. Manual segmentation performed by clinical experts has its drawbacks such as: the high time and concentration required from observers; and inter- and intra-observer variability. This is why in recent years automatic approaches to segment a prostate based on convolutional neural networks have emerged. Many of them have novel proposed architectures. In this paper I make an exhaustive study of several deep learning models by adjusting them to the task of prostate prediction. I do not use novel architectures, but focus my work more on how to train the networks. My approach is based on a ResNext101 3D encoder and a Unet3D decoder. I provide a study of the importance of resolutions in resampling data, something that no one else has done before.
