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

近年来，在多模态磁共振成像（MRI）扫描的成人脑肿瘤（胶质瘤）自动分割领域取得了很大进展，主要是由于多模态脑肿瘤图像分割基准（BRATS）挑战[1]的成功。然而，儿童脑肿瘤的分割领域仍然是一个相对未解决的问题。与成人脑肿瘤相比，儿童脑肿瘤通常表现在大脑的不同区域，并且具有不同的形态结构[2]。儿童脑肿瘤的分割，就像他们的成人同行一样，不是一项微不足道的任务，而且是一项需要在训练有素的最佳算法中添加新的功能来分割成人胶质瘤的任务。此外，可能有必要在手动分割的儿童脑肿瘤数据集上重新训练这些模型，或者实际上是新的模型，以达到所需的准确性。Alder Hey儿童医院目前拥有一个广泛的多模态儿童MRI脑部扫描数据集，这些数据集目前尚未使用，可以成为我们提出的深度学习模型的训练数据集的一部分。该项目的主要目标将是开发和应用现有的机器学习方法，这些方法在成人胶质瘤上取得了巨大的成功，并根据儿童脑肿瘤多模态磁共振图像扫描的典型特征进行调整。此外，可能有必要设想一种全新的网络架构，以解决儿童脑肿瘤与成人脑肿瘤在典型形态学特征或组织学亚型方面的差异。与此同时，我们的目标是拥有完全可操作的软件，它将建立在提议的机器学习管道之上，在Alder Hey配置，它将在扫描后立即自动分割儿科脑肿瘤，从而节省临床医生必须手动分割MRI切片的任务。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，以使临床医生更友好。这很可能涉及到一种边界盒方法，在这种方法中，临床医生可以通过绘制一个基本的边界盒来手动提取肿瘤区域，然后将其输入到分割软件中（从而只分割感兴趣的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，在机器学习模型/软件的实际实施方向上取得了很大的进展，这些模型/软件与世界各地不同医院MRI采集协议的可变性相结合。Brats数据集鼓励挑战者在一个非常方便的体积多模态数据集上竞争。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卓越的进展，但将这些模型应用于现实世界场景（如医院软件）的实用性很小。并不是所有的医院都以3D方式获取脑图像。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。这个项目的基本哲学精神是开发网络架构和建议的方法，这将考虑到儿科部门获取协议的可变性。第三个目标是致力于开发能够与当前最先进算法竞争的新型网络架构，同时对刚刚提到的基本精神给予应有的关注。[1] Bakas, Spyridon, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi， Russell Takeshi Shinohara等。“识别最好的机器

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