A multimodal convolutional neural network for prostate cancer diagnosis using volumetric multiparametric MRI and clinical features.

使用体积多参数 MRI 和临床特征进行前列腺癌诊断的多模态卷积神经网络。

• 批准号: 1921611
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1921611
• 关键词: multimodal; convolutional; neural; network; prostate

Aim: To build a deep convolutional neural network (CNN) for prostate cancer diagnosis that utilises volumetric multiparametric MRI (mpMRI) and clinical features.Motivation: Prostate cancer is the most common cancer amongst men in the UK, with incidence rates projected to rise by 12% in the period 2014-2035 [1]. 4-6 in 10 prostate cancer cases in the UK are diagnosed at a late stage [1]. By combining anatomical and functional information, studies like PROMIS ("Prostate MRI Imaging Study") [2] have shown that mpMRI of the prostate can radically improve prostate cancer diagnosis, non-invasively. Computer-aided diagnosis techniques can be used to obtain a fast, cost effective and reproducible diagnosis out of complex mpMRI data and other clinical data available [7]. In computer vision, deep CNNs are increasingly becoming the technique of choice for computer- aided diagnosis [4]. CNN architectures that can effectively combine mpMRI data is an understudied problem, as is combining imaging data with clinical data e.g. age, prostate specific antigen (PSA) level and gland volume. Furthermore, a CNN based approach would favour some of the applications of mpMRI highlighted in PROMIS. For example, a CNN that can assign a score to patients, between 0 (healthy) and 1 (unhealthy), may be used to triage patients according to the degree of abnormality reflected by the score.Background / Previous Work: Computer-aided diagnosis of prostate cancer has been an active area of research over the past decade. In [5], a novel two-stage (voxel classification / candidate classification) computer-aided detection workflow is developed based on handcrafted features. Alternatively, CNN based approaches "learn" the most discriminative features rather than requiring feature engineering. A comprehensive survey of over 300 contributions of deep learning in medical imaging can be found in [4]. A recent state of the art contribution uses a co-trained CNN for automated detection of prostate cancer in 2D images: T2-weighted (T2w) images and apparent diffusion coefficient (ADC) maps [8].Novelty: Most CNN approaches are only able to process 2D images whilst most medical data used in clinical practice consists of 3D volumes [6]. The main novelty of this project will be the use of volumetric T2w images, ADC maps and dynamic contrast enhanced (DCE) images. A further novelty will lie in the treatment of the DCE volumes. As these are collected over a series of timesteps, a voxel-wise statistical approach may be required to combine the DCE volumes time series into a single volume. Another novel aspect of this work will be an investigation of the effectiveness of combining imaging data with clinical data. A final novelty will be the application of our CNN output to triage, which to the best of our knowledge, is an area that is unexplored to date and can have a substantial clinical impact.

目的：建立一个深度卷积神经网络（CNN）用于前列腺癌诊断，利用体积多参数MRI（mpMRI）和临床特征。动机：前列腺癌是英国男性中最常见的癌症，预计在2014-2035年期间发病率将上升12%[1]。4-6在英国，有10例前列腺癌病例在晚期被诊断出来[1]。通过结合解剖和功能信息，PROMIS（“前列腺MRI成像研究”）[2]等研究表明，前列腺的mpMRI可以从根本上提高前列腺癌的诊断，非侵入性。计算机辅助诊断技术可用于从复杂的mpMRI数据和其他可用的临床数据中获得快速、经济有效和可重现的诊断[7]。在计算机视觉中，深度CNN越来越成为计算机辅助诊断的首选技术[4]。可以有效地结合联合收割机mpMRI数据的CNN架构是一个未充分研究的问题，将成像数据与临床数据（例如年龄、前列腺特异性抗原（PSA）水平和腺体体积）结合也是如此。此外，基于CNN的方法将有利于PROMIS中强调的mpMRI的一些应用。例如，CNN可以为患者分配一个分数，在0（健康）和1（不健康）之间，可以用于根据分数反映的异常程度对患者进行分诊。背景/以前的工作：在过去的十年中，前列腺癌的计算机辅助诊断一直是一个活跃的研究领域。在[5]中，基于手工特征开发了一种新的两阶段（体素分类/候选分类）计算机辅助检测工作流程。或者，基于CNN的方法“学习”最具鉴别力的特征，而不需要特征工程。对深度学习在医学成像中的300多项贡献的全面调查可以在[4]中找到。最新的最新技术贡献使用共同训练的CNN来自动检测2D图像中的前列腺癌：T2加权（T2 w）图像和表观扩散系数（ADC）图[8]。新奇：大多数CNN方法只能处理2D图像，而临床实践中使用的大多数医学数据由3D体积组成[6]。该项目的主要新奇之处在于使用体积T2 w图像、ADC图和动态对比度增强（DCE）图像。另一个新奇在于对DCE卷的处理。由于这些是在一系列时间步长上收集的，因此可能需要逐体素统计方法来将DCE体积时间序列联合收割机组合成单个体积。这项工作的另一个新的方面将是结合成像数据与临床数据的有效性的调查。最后一个新奇将是将我们的CNN输出应用于分诊，据我们所知，这是一个迄今为止尚未探索的领域，可以产生重大的临床影响。