Segmentation of Pediatric Brain Tumors on Multi-Modal MRI Data using Deep Learning Approaches

使用深度学习方法对多模态 MRI 数据进行儿童脑肿瘤分割

• 批准号: 2439784
• 金额: --
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2439784
• 关键词: Segmentation; Pediatric; Brain; Tumors; Multi

Over recent years great progress has been made in the field of automated segmentation of adult brain tumors (gliomas) on multi-modal Magnetic Resonance Imaging (MRI) scans, primarily due to the success of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) challenge [1]. However, the field of pediatric brain tumor segmentation is still a relatively unaddressed one. In contrast to adult brain tumors, pediatric brain tumors typically manifest in different regions of the brain, as well as possessing different morphological structures [2]. The segmentation of pediatric brain tumors, like their adult counterparts, is not a trivial task, and is one which requires novel additions to the best performing algorithms trained to segment adult gliomas. Furthermore, it may be necessary to retrain those models, or indeed novel ones, on a dataset of manually segmented pediatric brain tumors to achieve the desired accuracy. Alder Hey Children's Hospital is currently in possession of a wide dataset of multi-modal pediatric MRI brain scans which are currently unused, and which could form part of the training dataset for our proposed deep learning models.The principal aim of the project will be to develop and apply existing machine learning approaches which have demonstrated great success on adult gliomas, and tailor them to address the typical characteristics of pediatric brain tumor scans on multi-modal MR images. Furthermore, it may be necessary to conceive of entirely novel network architectures to address the difference in typical morphological characteristics or histological subtypes between pediatric brain tumors and their adult counterparts. In conjunction with this, we also aim to have fully operational software, which will be built on top of the proposed machine learning pipeline, configured at Alder Hey and which will automate the segmentation of pediatric brain tumors immediately post scan, thereby saving the clinician the task of having to manually segment an MRI slice-wise.A secondary aim will be to work closely with a clinician to tailor said software to a more appropriate format which will aim to be more clinician friendly. This will most likely involve a bounding-box approach in which a clinician can manually extract the tumorous region by drawing a rudimentary bounding box which will then be fed to the segmentation software (thereby only segmenting the region of interest and reducing computational cost). While on this note I feel compelled to add that it is of much importance to me and this project that large strides are taken in the direction of practical implementation of machine learning models/software which work in conjunction with the variability in MRI acquisition protocol across different hospitals across the world. The Brats dataset encourages challengers to compete on a very convenient volumetric multi-modal dataset. Although this challenge has yielded excellent advances in computer vison and medical imaging segmentation tasks, the practicality of applying those models to real-word scenarios, as software for hospitals, is minimal. Not all hospitals acquire brain images as 3D volumes for all modalities. And so, most of the currently existing segmentation models which succeed in reaching state of the art results on the Brats dataset would have little or no success in departments where the imaging protocol is not necessarily volumetric. A fundamental philosophical ethos of this project is to develop network architectures and proposed approaches which will account for the variability in acquisition protocols across pediatric departments. A tertiary aim will be to work on developing novel network architectures which can compete with the current state of the art algorithms whilst paying due attention to the fundamental ethos just mentioned. [1] Bakas, Spyridon, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara et al. "Identifying the best machine l

近年来，多模态磁共振成像 (MRI) 扫描成人脑肿瘤（胶质瘤）自动分割领域取得了巨大进展，这主要归功于多模态脑肿瘤图像分割基准 (BRATS) 挑战的成功 [1]。然而，小儿脑肿瘤分割领域仍然是一个相对未得到解决的领域。与成人脑肿瘤相比，儿童脑肿瘤通常出现在大脑的不同区域，并且具有不同的形态结构[2]。与成人脑肿瘤的分割一样，儿科脑肿瘤的分割并不是一项简单的任务，并且需要对经过训练以分割成人神经胶质瘤的最佳性能算法进行新的补充。此外，可能有必要在手动分割的儿科脑肿瘤数据集上重新训练这些模型，甚至是新颖的模型，以达到所需的准确性。 Alder Hey 儿童医院目前拥有广泛的多模态儿科 MRI 脑部扫描数据集，这些数据集目前尚未使用，可以构成我们提出的深度学习模型训练数据集的一部分。该项目的主要目的是开发和应用现有的机器学习方法，这些方法已在成人神经胶质瘤上取得了巨大成功，并对其进行定制，以解决多模态 MR 图像上儿科脑肿瘤扫描的典型特征。此外，可能有必要构思全新的网络架构，以解决儿童脑肿瘤与成人脑肿瘤之间典型形态特征或组织学亚型的差异。与此相结合，我们还旨在拥有完全可操作的软件，该软件将建立在拟议的机器学习管道之上，在 Alder Hey 配置，并将在扫描后立即自动分割儿科脑肿瘤，从而使临床医生免于手动逐层分割 MRI 的任务。第二个目标是与临床医生密切合作，将所述软件定制为更合适的格式，旨在更 临床医生友好。这很可能涉及边界框方法，其中临床医生可以通过绘制基本边界框来手动提取肿瘤区域，然后将其馈送到分割软件（从而仅分割感兴趣区域并减少计算成本）。在这一点上，我觉得有必要补充一点，对我和这个项目来说非常重要的是，在机器学习模型/软件的实际实施方向上取得了长足的进步，这些模型/软件与世界各地不同医院的 MRI 采集协议的变化相结合。 Brats 数据集鼓励挑战者在非常方便的体积多模态数据集上进行竞争。尽管这一挑战在计算机视觉和医学成像分割任务方面取得了巨大进步，但将这些模型作为医院软件应用到实际场景中的实用性却很小。并非所有医院都会针对所有模式获取 3D 体积的大脑图像。因此，目前大多数在 Brats 数据集上成功达到最先进结果的现有分割模型在成像协议不一定是体积的部门中几乎没有成功。该项目的基本理念是开发网络架构和提出的方法，以解决儿科各部门采集协议的可变性。第三个目标是致力于开发新颖的网络架构，该架构可以与当前最先进的算法竞争，同时适当关注刚刚提到的基本精神。 [1] Bakas、Spyridon、Mauricio Reyes、Andras Jakab、Stefan Bauer、Markus Rempfler、Alessandro Crimi、Russell Takeshi Shinohara 等。 “确定最佳机器 l