Automated segmentation of brain tumor based on improved U-Net with residual units

Multimedia Tools and Applications - The study presents a new approach to automate segmentation of clinically significant brain tumor and, to a certain extent, addresses two major issues associated...     

MRI segmentation fusion for brain tumor detection

The process of manually generating precise segmentations of brain tumors from magnetic resonance images (MRI) is time-consuming and error-prone. We present a new algorithm, Potential Field Segmentation (PFS), and propose the use of ensemble approaches that combine the results generated by PFS and other methods to achieve a fused segmentation. For the PFS method, we build on our recently proposed clustering algorithm, Potential Field Clustering, which is based on an analogy with the concept of potential field in Physics. We view the intensity of a pixel in an MRI as a “mass” that creates a potential field. Specifically, for each pixel in the MRI, the potential field is computed and, if smaller than an adaptive potential threshold, the pixel is associated with the tumor region. This “small potential” segmentation criterion is intuitively valid because tumor pixels have larger “mass” and thus the potential of surrounding regions is also much larger than in other regions of smaller or no “mass”. We evaluate the performance of the different methods, including the ensemble approaches, on the publicly available Brain Tumor Image Segmentation (BRATS) MRI benchmark database.     

基于特征选择与残差融合的肝肿瘤分割模型

目的 高效的肝肿瘤计算机断层扫描(computed tomography, CT)图像自动分割方法是临床实践的迫切需求,但由于肝肿瘤边界不清晰、体积相对较小且位置无规律,要求分割模型能够细致准确地发掘类间差异。对此,本文提出一种基于特征选择与残差融合的2D肝肿瘤分割模型,提高了2D模型在肝肿瘤分割任务中的表现。方法该模型通过注意力机制对U-Net瓶颈特征及跳跃链接进行优化,为符合肝肿瘤分割任务特点优化传统注意力模块进,提出以全局特征压缩操作(global feature squeeze, GFS)为基础的瓶颈特征选择模块,即全局特征选择模块(feature selection module, FS)和邻近特征选择模块(neighbor feature selection module, NFS)。跳跃链接先通过空间注意力模块(spatial attention module, SAM)进行特征重标定,再通过空间特征残差融合(spatial feature residual fusion module, SFRF)模块解决前后空间特征的语义不匹配问题,在保持低复杂度的同时使特征高效表达。...     

深层聚合残差密集网络的超声图像左心室分割

目的 超声图像是临床医学中应用最广泛的医学图像之一,但左心室超声图像一般具有强噪声、弱边缘和组织结构复杂等问题,其图像分割难度较大。临床上需要一种效率高、质量好的超声图像左心室分割算法。本文提出一种基于深层聚合残差密集网络（deep layer aggregation for residual dense network,DLA-RDNet）的超声图像左心室分割算法。方法 对获取的超声图像进行形态学操作,定位目标区域,得到目标图像。构建残差密集网络（residual dense network,RDNet）用于提取图像特征,并将RDNet得到的层次信息通过深层聚合（deep layer aggregation,DLA）的方式紧密融合到一起,得到分割网络DLA-RDNet,用于实现对超声图像左心室的精确分割。通过深监督（deep supervision,DS）方式为网络剪枝,简化网络结构,提升网络运行速度。结果 数据测试集的实验结果表明,所提算法平均准确率为95.68%,平均交并比为97.13%,平均相似性系数为97.15%,平均垂直距离为0.31 mm,分割轮廓合格率为99.32%。与6种分割算法相比,所提算法的分割精度更高。在测试阶段,每幅图像仅需不到1 s的时间即可完成分割,远远超出了专业医生的分割速度。结论 提出了一种深层聚合残差密集神经网络对超声图像左心室进行分割,通过主、客观对比实验表明本文算法的有效性,能够较对比方法更实时准确地对超声图像左心室进行分割,符合临床医学中超声图像左心室分割的需求。     

基于密集Inception的单图像超分辨率重建方法

近几年,基于卷积神经网络（CNN）的单图像超分辨率（SR）重建方法成为了主流。通常情况下,重建模型的网络层数越深,提取的特征越多,重建效果越好;然而随着网络层数的加深,不仅会出现梯度消失的问题,还会显著增加参数量,增加训练的难度。针对以上问题,提出了一种基于密集Inception的单图像SR重建方法。该方法引入Inception-残差网络（Inception-ResNet）结构提取图像特征,全局采用简化后的密集网络,且仅构建每一个模块输出到重建层的路径,从而避免产生冗余数据来增加计算量。在放大倍数为4时,采用数据集Set5测试模型性能,结果显示与超深卷积神经网络的图像超分辨率（VDSR）相比,所提方法的结构相似性（SSIM）高了0.013 6;与基于多尺度残差网络的图像SR（MSRN）相比,SSIM高了0.002 9,模型参数量少了78%。实验结果表明,所提方法在保证模型的深度和宽度的情况下,显著减少了参数量,从而降低了训练的难度,而且取得了比对比方法更好的峰值信噪比（PSNR）和SSIM。     

CBSN: Comparative measures of normalization techniques for brain tumor segmentation using SRCNet

Multimedia Tools and Applications - The segmentation of tumors in the brain MRI scans is a difficult job for doctors and radiologists. The segmentation done by different medical experts may also...     

Deep learning based enhanced tumor segmentation approach for MR brain images

Automation in medical industry has become one of the necessities in today’s medical scenario. Radiologists/physicians need such automation techniques for accurate diagnosis and treatment planning. Automatic segmentation of tumor portion from Magnetic Resonance (MR) brain images is a challenging task. Several methodologies have been developed with an objective to enhance the segmentation efficiency of the automated system. However, there is always scope for improvement in the segmentation process of medical image analysis. In this work, deep learning-based approach is proposed for brain tumor image segmentation. The proposed method includes the concept of Stationary Wavelet Transform (SWT) and new Growing Convolution Neural Network (GCNN). The significant objective of this work is to enhance the accuracy of the conventional system. A comparative analysis with Support Vector Machine (SVM) and Convolution Neural Network (CNN) is carried out in this work. The experimental results prove that the proposed technique has outperformed SVM and CNN in terms of accuracy, PSNR, MSE and other performance parameters.     

MRI脑肿瘤图像分割的深度学习方法综述

磁共振成像（MRI）作为一种典型的非侵入式成像技术，可产生高质量的无损伤和无颅骨伪影的脑影像，为脑肿瘤的诊断和治疗提供更为全面的信息，是脑肿瘤诊疗的主要技术手段。MRI脑肿瘤自动分割利用计算机技术从多模态脑影像中自动将肿瘤区（坏死区、水肿区、非增强肿瘤区和增强肿瘤区）和正常组织区进行分割和标注，对于辅助脑肿瘤的诊疗具有重要作用。本文对MRI脑肿瘤图像分割的深度学习方法进行了总结与分析，给出了各类方法的基本思想、网络架构形式、代表性改进方案以及优缺点总结等，并给出了部分典型方法在BraTS（multimodal brain tumor segmentation）数据集上的性能表现与分析结果。通过对该领域研究方法进行综述，对现有基于深度学习的MRI脑肿瘤分割研究方法进行了梳理，作为新的发展方向，MRI脑肿瘤图像分割的深度学习方法较传统方法已取得明显的性能提升，已成为领域主流方法并持续展现出良好的发展前景，有助于进一步推动MRI脑肿瘤分割在临床诊疗上的应用。     

A new fuzzy clustering algorithm for the segmentation of brain tumor

This paper introduces a new method of clustering algorithm based on interval-valued intuitionistic fuzzy sets (IVIFSs) generated from intuitionistic fuzzy sets to analyze tumor in magnetic resonance (MR) images by reducing time complexity and errors. Based on fuzzy clustering, during the segmentation process one can consider numerous cases of uncertainty involving in membership function, distance measure, fuzzifier, and so on. Due to poor illumination of medical images, uncertainty emerges in their gray levels. This paper concentrates on uncertainty in the allotment of values to the membership function of the uncertain pixels. Proposed method initially pre-processes the brain MR images to remove noise, standardize intensity, and extract brain region. Subsequently IVIFSs are constructed to utilize in the clustering algorithm. Results are compared with the segmented images obtained using histogram thresholding, k-means, fuzzy c-means, intuitionistic fuzzy c-means, and interval type-2 fuzzy c-means algorithms and it has been proven that the proposed method is more effective.     

面向多模态磁共振脑瘤图像的小样本分割方法

针对脑肿瘤磁共振成像(MRI)模态多、训练数据少、类别不平衡以及各个私有数据库差异大等导致分割困难的问题,引入小样本分割方法,并提出基于U-net的原型网络(PU-net)模型用以对脑肿瘤磁共振(MR)图像进行分割.首先对U-net的结构进行调整来提取各类瘤体的特征用以计算原型;然后在原型网络的基础上,逐像素利用原型对...     

MRI脑肿瘤图像分割研究进展及挑战

脑肿瘤分割是医学图像处理中的一项重要内容,其目的是辅助医生做出准确的诊断和治疗,在临床脑部医学领域具有重要的实用价值。核磁共振成像（MRI）是临床医生研究脑部组织结构的主要影像学工具,为了使更多研究者对MRI脑肿瘤图像分割理论及其发展进行探索,本文对该领域研究现状进行综述。首先总结了用于MRI脑肿瘤图像分割的方法,并对现有方法进行了分类,即分为监督分割和非监督分割;然后重点综述了基于深度学习的脑肿瘤分割方法,在研究其关键技术基础上归纳了优化策略;最后介绍了脑肿瘤分割（BraTS）挑战,并结合挑战中所用方法展望了脑肿瘤分割领域未来的发展趋势。MRI脑肿瘤图像分割领域的研究已经取得了一些显著进展,尤其是深度学习的发展为该领域的研究提供了新的思路。但由于脑肿瘤在大小、形状和位置方面的高度变化,以及脑肿瘤图像数据有限且类别不平衡等问题,使得脑肿瘤图像分割仍是一个极具挑战的课题。由于分割过程缺乏可解释性和透明性,如何将全自动分割方法应用于临床试验,还需要进行深入研究。     

基于同一特征空间的多模态脑肿瘤分割方法

脑胶质瘤的分割依赖多种模态的核磁共振成像（MRI）的影像。基于卷积神经网络（CNN）的分割算法往往是在固定的多种模态影像上进行训练和测试,这忽略了模态数据缺失或增加问题。针对这个问题,提出了将不同模态的图像通过CNN映射到同一特征空间下并利用同一特征空间下的特征来分割肿瘤的方法。首先,不同模态的数据经过同一深度CNN提取特征;然后,将不同模态的特征连接起来,经过全连接层实现特征融合;最后,利用融合的特征实现脑肿瘤分割。模型采用BRATS2015数据集进行训练和测试,并使用Dice系数对模型进行验证。实验结果表明了所提模型能有效缓解数据缺失问题。同时,该模型较多模态联合的方法更加灵活,能够应对模态数据增加问题。     

Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation

The segmentation of brain tumor plays an important role in diagnosis, treatment planning, and surgical simulation. The precise segmentation of brain tumor can help clinicians obtain its location, size, and shape information. We propose a fully automatic brain tumor segmentation method based on kernel sparse coding. It is validated with 3D multiple-modality magnetic resonance imaging (MRI). In this method, MRI images are pre-processed first to reduce the noise, and then kernel dictionary learning is used to extract the nonlinear features to construct five adaptive dictionaries for healthy tissues, necrosis, edema, non-enhancing tumor, and enhancing tumor tissues. Sparse coding is performed on the feature vectors extracted from the original MRI images, which are a patch of m×m×m around the voxel. A kernel-clustering algorithm based on dictionary learning is developed to code the voxels. In the end, morphological filtering is used to fill in the area among multiple connected components to improve the segmentation quality. To assess the segmentation performance, the segmentation results are uploaded to the online evaluation system where the evaluation metrics dice score, positive predictive value (PPV), sensitivity, and kappa are used. The results demonstrate that the proposed method has good performance on the complete tumor region (dice: 0.83; PPV: 0.84; sensitivity: 0.82), while slightly worse performance on the tumor core (dice: 0.69; PPV: 0.76; sensitivity: 0.80) and enhancing tumor (dice: 0.58; PPV: 0.60; sensitivity: 0.65). It is competitive to the other groups in the brain tumor segmentation challenge. Therefore, it is a potential method in differentiation of healthy and pathological tissues.     

Dual-branch residual network for lung nodule segmentation

An accurate segmentation of lung nodules in computed tomography (CT) images is critical to lung cancer analysis and diagnosis. However, due to the variety of lung nodules and the similarity of visual characteristics between nodules and their surroundings, a robust segmentation of nodules becomes a challenging problem. In this study, we propose the Dual-branch Residual Network (DB-ResNet) which is a data-driven model. Our approach integrates two new schemes to improve the generalization capability of the model: (1) the proposed model can simultaneously capture multi-view and multi-scale features of different nodules in CT images; (2) we combine the features of the intensity and the convolutional neural networks (CNN). We propose a pooling method, called the central intensity-pooling layer (CIP), to extract the intensity features of the center voxel of the block, and then use the CNN to obtain the convolutional features of the center voxel of the block. In addition, we designed a weighted sampling strategy based on the boundary of nodules for the selection of those voxels using the weighting score, to increase the accuracy of the model. The proposed method has been extensively evaluated on the LIDC-IDRI dataset containing 986 nodules. Experimental results show that the DB-ResNet achieves superior segmentation performance with the dice similarity coefficient (DSC) of 82.74% on the dataset. Moreover, we compared our results with those of four radiologists on the same dataset. The comparison showed that our DSC was 0.49% higher than that of human experts. This proves that our proposed method is as good as the experienced radiologist.     

Automated brain tumor segmentation on multi-modal MR image using SegNet

Computational Visual Media - The potential of improving disease detection and treatment planning comes with accurate and fully automatic algorithms for brain tumor segmentation. Glioma, a type of...     

融合跨阶段深度学习的脑肿瘤MRI图像分割

目的 磁共振成像(magnetic resonance imaging, MRI)作为一种非侵入性的软组织对比成像方式,可以提供有关脑肿瘤的形状、大小和位置等有价值的信息,是用于脑肿瘤患者检查的主要方法,在脑肿瘤分割任务中发挥着重要作用。由于脑肿瘤本身复杂多变的形态、模糊的边界、低对比度以及样本梯度复杂等问题,导致高精度脑肿瘤MRI图像分割非常具有挑战性,目前主要依靠专业医师手动分割,费时且可重复性差。对此,本文提出一种基于U-Net的改进模型,即CSPU-Net(cross stage partial U-Net)脑肿瘤分割网络,以实现高精度的脑肿瘤MRI图像分割。方法 CSPU-Net在U-Net结构的上下采样中分别加入两种跨阶段局部网络结构(cross stage partial module, CSP)提取图像特征,结合GDL(general Dice loss)和WCE(weighted cross entropy)两种损失函数解决训练样本类别不平衡问题。结果 在BraTS (brain tumor segmentation) 2018和BraTS 2019两个数据集上进行实...     

自适应模态融合双编码器MRI脑肿瘤分割网络

目的 评估肿瘤的恶性程度是临床诊断中的一项具有挑战性的任务。因脑肿瘤的磁共振成像呈现出不同的形状和大小,肿瘤的边缘模糊不清,导致肿瘤分割具有挑战性。为有效辅助临床医生进行肿瘤评估和诊断,提高脑肿瘤分割精度,提出一种自适应模态融合双编码器分割网络D3D-Net(double3DNet)。方法 本文提出的网络使用多个编码器和特定的特征融合的策略,采用双层编码器用于充分提取不同模态组合的图像特征,并在编码部分利用特定的融合策略将来自上下两个子编码器的特征信息充分融合,去除冗余特征。此外,在编码解码部分使用扩张多纤维模块在不增加计算开销的前提下捕获多尺度的图像特征,并引入注意力门控以保留细节信息。结果 采用BraTS2018(brain tumor segmentation 2018)、BraTS2019和BraTS2020数据集对D3D-Net网络进行训练和测试,并进行了消融实验。在BraTS2018数据集上,本模型在增强肿瘤、整个肿瘤、肿瘤核心的平均Dice值与3D U-Net相比分别提高了3.6%,1.0%,11.5%,与DMF-Net(dilatedmulti-fibernetwork...     

利用残差密集网络的运动模糊复原方法

针对图像生成过程中由于物体运动或相机抖动产生的运动模糊问题,提出了利用残差密集网络的运动模糊图像复原方法。设计对抗网络结构,以残差密集网络为生成器,通过长短连接实现不同层次特征的融合,生成复原图像,以深度卷积网络为判别器,判断图像真伪,在生成器和判别器的对抗中提高网络性能;采用对抗损失和内容损失结合的损失函数,提高网络的复原效果;以端到端的方式,省略模糊核的估计过程,输入模糊图像直接获取复原图像。实验结果表明,该方法能够取得较好的复原效果。     

残差密集空间金字塔网络的城市遥感图像分割

目的 遥感图像语义分割是根据土地覆盖类型对图像中每个像素进行分类,是遥感图像处理领域的一个重要研究方向。由于遥感图像包含的地物尺度差别大、地物边界复杂等原因,准确提取遥感图像特征具有一定难度,使得精确分割遥感图像比较困难。卷积神经网络因其自主分层提取图像特征的特点逐步成为图像处理领域的主流算法,本文将基于残差密集空间金字塔的卷积神经网络应用于城市地区遥感图像分割,以提升高分辨率城市地区遥感影像语义分割的精度。方法 模型将带孔卷积引入残差网络,代替网络中的下采样操作,在扩大特征图感受野的同时能够保持特征图尺寸不变;模型基于密集连接机制级联空间金字塔结构各分支,每个分支的输出都有更加密集的感受野信息;模型利用跳线连接跨层融合网络特征,结合网络中的高层语义特征和低层纹理特征恢复空间信息。结果 基于ISPRS （International Society for Photogrammetry and Remote Sensing） Vaihingen地区遥感数据集展开充分的实验研究,实验结果表明,本文模型在6种不同的地物分类上的平均交并比和平均F₁值分别达到69.88%和81.39%,性能在数学指标和视觉效果上均优于SegNet、pix2pix、Res-shuffling-Net以及SDFCN （symmetrical dense-shortcut fully convolutional network）算法。结论 将密集连接改进空间金字塔池化网络应用于高分辨率遥感图像语义分割,该模型利用了遥感图像不同尺度下的特征、高层语义信息和低层纹理信息,有效提升了城市地区遥感图像分割精度。