Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks application
Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks application
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DOI：
10.1371/journal.pone.0237213
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发表时间：
2020-08-14
期刊：
影响因子：
3.7
通讯作者：
Papageorgiou, Konstantinos
中科院分区：
文献类型：
--
作者：
Papandrianos, Nikolaos;Papageorgiou, Elpiniki;Papageorgiou, Konstantinos
骨转移是前列腺癌最常见的疾病之一，前列腺造影成像对临床诊断骨转移尤为重要。到目前为止，关于机器学习的应用已经进行了最少的研究，重点是现代有效的卷积神经网络（CNN）算法，用于从骨组织造影图像诊断前列腺癌转移。深度学习是机器学习的突破性技术进步，其优势和卓越的能力尚未被充分研究，其在医学图像分析领域的计算机辅助诊断系统中的应用，例如全身扫描中的骨转移分类问题。特别是，CNN由于其识别复杂视觉模式的能力而获得了极大的关注，与人类感知的运作方式相同。考虑到深度学习领域的所有这些新增强，探索了一组更简单，更快，更准确的CNN架构，旨在对骨转移性前列腺癌进行分类。这项研究有两个目标：创建并展示一组简单但强大的CNN模型，用于将全身扫描自动分类为恶性（骨转移）或健康两类，仅使用输入级的扫描。通过对CNN超参数选择和微调的细致探索，选择了分类精度最好的架构。因此，使用来自前列腺癌患者的骨扫描，产生具有用于骨转移诊断的改进分类能力的CNN模型。实现的分类测试准确率为97.38%，而平均灵敏度约为95.8%。最后，将性能最好的CNN方法与其他流行且知名的用于医学成像的CNN架构进行比较，如VGG16，ResNet50，GoogleNet和MobileNet。分类结果表明，所提出的基于CNN的方法优于核医学中流行的CNN方法，用于骨转移性前列腺癌的诊断。
Bone metastasis is one of the most frequent diseases in prostate cancer; scintigraphy imaging is particularly important for the clinical diagnosis of bone metastasis. Up to date, minimal research has been conducted regarding the application of machine learning with emphasis on modern efficient convolutional neural networks (CNNs) algorithms, for the diagnosis of prostate cancer metastasis from bone scintigraphy images. The advantageous and outstanding capabilities of deep learning, machine learning's groundbreaking technological advancement, have not yet been fully investigated regarding their application in computer-aided diagnosis systems in the field of medical image analysis, such as the problem of bone metastasis classification in whole-body scans. In particular, CNNs are gaining great attention due to their ability to recognize complex visual patterns, in the same way as human perception operates. Considering all these new enhancements in the field of deep learning, a set of simpler, faster and more accurate CNN architectures, designed for classification of metastatic prostate cancer in bones, is explored. This research study has a two-fold goal: to create and also demonstrate a set of simple but robust CNN models for automatic classification of whole-body scans in two categories, malignant (bone metastasis) or healthy, using solely the scans at the input level. Through a meticulous exploration of CNN hyper-parameter selection and fine-tuning, the best architecture is selected with respect to classification accuracy. Thus a CNN model with improved classification capabilities for bone metastasis diagnosis is produced, using bone scans from prostate cancer patients. The achieved classification testing accuracy is 97.38%, whereas the average sensitivity is approximately 95.8%. Finally, the best-performing CNN method is compared to other popular and well-known CNN architectures used for medical imaging, like VGG16, ResNet50, GoogleNet and MobileNet. The classification results show that the proposed CNN-based approach outperforms the popular CNN methods in nuclear medicine for metastatic prostate cancer diagnosis in bones.