Deep learning based neural network application for automatic ultrasonic computed tomographic bone image segmentation

Deep-learning techniques have led to technological progress in the area of medical imaging segmentation especially in the ultrasound domain. In this paper, the main goal of this study is to optimize a deep-learning-based neural network architecture for automatic segmentation in Ultrasonic Computed Tomography (USCT) bone images in a short time process. The proposed method is based on an end to end neural network architecture. First, the novelty is shown by the improvement of Variable Structure Model of Neuron (VSMN), which is trained for both USCT noise removal and dataset augmentation. Second, a VGG-SegNet neural network architecture is trained and tested on new USCT images not seen before for automatic bone segmentation. Therefore, we offer a free USCT dataset. In addition, the proposed model is implemented on both the CPU and the GPU, hence overcoming previous works by a value of 97.38% and 96% for training and validation and achieving high segmentation accuracy for testing with a small error of 0.006, in a short time process. The suggested method demonstrates its ability to augment USCT data and then to automatically segment USCT bone structures achieving excellent accuracy outperforming the state of the art.

     

联合语义分割与边缘重建的深度学习图像修复

目的 传统图像修复方法缺乏对图像高级语义的理解,只能应对结构纹理简单的小面积受损。现有的端到端深度学习图像修复方法在大量训练图像的支持下克服了上述局限性,但由于这些方法试图在约束不足的情况下恢复整个目标,修复的图像往往存在边界模糊和结构扭曲问题。对此,本文提出一种语义分割结构与边缘结构联合指导的深度学习图像修复方法。方法 该方法将图像修复任务分解为语义分割重建、边缘重建和内容补全3个阶段。首先重建缺失区域的语义分割结构,然后利用重建的语义分割结构指导缺失区域边缘结构的重建,最后利用重建的语义分割结构与边缘结构联合指导图像缺失区域内容的补全。结果 在CelebAMask-HQ(celebfaces attributes mask high quality)人脸数据集和Cityscapes城市景观数据集上,将本文方法与其他先进的图像修复方法进行对比实验。在掩膜比例为50%■60%的情况下,与性能第2的方法相比,本文方法在Celebamask-HQ数据集上的平均绝对误差降低了4.5%,峰值信噪比提高了1.6%,结构相似性提高了1.7%;在Cityscapes数据集上平均绝对误差降低了4.2%...     

Review of liver segmentation and computer assisted detection/diagnosis methods in computed tomography

Computed tomography (CT) imaging remains the most utilized modality for liver-related cancer screening and treatment monitoring purposes. Liver, liver tumor and liver vasculature segmentation from CT data is a prerequisite for treatment planning and computer assisted detection/diagnosis systems. In this paper, we present a survey on liver, liver tumor and liver vasculature segmentation methods that are using CT images, recent methods presented in the literature are viewed and discussed along with positives, negatives and statistical performance of these methods. Liver computer assisted detection/diagnosis systems will also be discussed along with their limitations and possible ways of improvement. In this paper, we concluded that although there is still room for improvement, automatic liver segmentation methods have become comparable to human segmentation. However, the performance of liver tumor segmentation methods can be considered lower than expected in both automatic and semi-automatic methods. Furthermore, it can be seen that most computer assisted detection/diagnosis systems require manual segmentation of liver and liver tumors, limiting clinical applicability of these systems. Liver, liver tumor and liver vasculature segmentation is still an open problem since various weaknesses and drawbacks of these methods can still be addressed and improved especially in tumor and vasculature segmentation along with computer assisted detection/diagnosis systems.     

Deep residual learning for image steganalysis

Image steganalysis is to discriminate innocent images and those suspected images with hidden messages. This task is very challenging for modern adaptive steganography, since modifications due to message hiding are extremely small. Recent studies show that Convolutional Neural Networks (CNN) have demonstrated superior performances than traditional steganalytic methods. Following this idea, we propose a novel CNN model for image steganalysis based on residual learning. The proposed Deep Residual learning based Network (DRN) shows two attractive properties than existing CNN based methods. First, the model usually contains a large number of network layers, which proves to be effective to capture the complex statistics of digital images. Second, the residual learning in DRN preserves the stego signal coming from secret messages, which is extremely beneficial for the discrimination of cover images and stego images. Comprehensive experiments on standard dataset show that the DRN model can detect the state of arts steganographic algorithms at a high accuracy. It also outperforms the classical rich model method and several recently proposed CNN based methods.     

深度卷积神经网络图像语义分割研究进展

在计算机视觉领域中,语义分割是场景解析和行为识别的关键任务,基于深度卷积神经网络的图像语义分割方法已经取得突破性进展。语义分割的任务是对图像中的每一个像素分配所属的类别标签,属于像素级的图像理解。目标检测仅定位目标的边界框,而语义分割需要分割出图像中的目标。本文首先分析和描述了语义分割领域存在的困难和挑战,介绍了语义分割算法性能评价的常用数据集和客观评测指标。然后,归纳和总结了现阶段主流的基于深度卷积神经网络的图像语义分割方法的国内外研究现状,依据网络训练是否需要像素级的标注图像,将现有方法分为基于监督学习的语义分割和基于弱监督学习的语义分割两类,详细阐述并分析这两类方法各自的优势和不足。本文在PASCAL VOC（pattern analysis, statistical modelling and computational learning visual object classes）2012数据集上比较了部分监督学习和弱监督学习的语义分割模型,并给出了监督学习模型和弱监督学习模型中的最优方法,以及对应的MIoU（mean intersection-over-union）。最后,指出了图像语义分割领域未来可能的热点方向。     

Accelerated liver tumor segmentation in four-phase computed tomography images

Segmentation and volume measurement of liver tumor are important tasks for surgical planning and cancer follow-up. In this work, a segmentation method from four-phase computed tomography images is proposed. It is based on the combination of the Expectation-Maximization algorithm and the Hidden Markov Random Fields. The latter considers the spatial information given by voxel neighbors of two contrast phases. The segmentation algorithm is applied on a volume of interest that decreases the number of processed voxels. To accelerate the classification steps within the segmentation process, a Bootstrap resampling scheme is also adopted. It consists in selecting randomly an optimal representative set of voxels. The experimental results carried out on three clinical datasets show the performance of our liver tumor segmentation method. It has been notably observed that the computing time of the classification algorithm is reduced without any significant impact on the segmentation accuracy.     

Deep learning techniques for tumor segmentation: a review

Recently, deep learning, especially convolutional neural networks, has achieved the remarkable results in natural image classification and segmentation. At the same time, in the field of medical image segmentation, researchers use deep learning techniques for tasks such as tumor segmentation, cell segmentation, and organ segmentation. Automatic tumor segmentation plays an important role in radiotherapy and clinical practice and is the basis for the implementation of follow-up treatment programs. This paper reviews the tumor segmentation methods based on deep learning in recent years. We first introduce the common medical image types and the evaluation criteria of segmentation results in tumor segmentation. Then, we review the tumor segmentation methods based on deep learning from technique view and tumor view, respectively. The technique view reviews the researches from the architecture of the deep learning and the tumor view reviews from the type of tumors.

     

Deep learning for computer-aided medical diagnosis

Multimedia Tools and Applications -     

Deep learning based approach for digitized herbarium specimen segmentation

Multimedia Tools and Applications - As herbarium specimens are largely digitized and freely available in online portals, botanists aim to examine their taxonomic aspects to identify the plant...     

An external field prior for the hidden Potts model with application to cone-beam computed tomography

In images with low contrast-to-noise ratio (CNR), the information gain from the observed pixel values can be insufficient to distinguish foreground objects. A Bayesian approach to this problem is to incorporate prior information about the objects into a statistical model. A method for representing spatial prior information as an external field in a hidden Potts model is introduced. This prior distribution over the latent pixel labels is a mixture of Gaussian fields, centred on the positions of the objects at a previous point in time. It is particularly applicable in longitudinal imaging studies, where the manual segmentation of one image can be used as a prior for automatic segmentation of subsequent images. The method is demonstrated by application to cone-beam computed tomography (CT), an imaging modality that exhibits distortions in pixel values due to X-ray scatter. The external field prior results in a substantial improvement in segmentation accuracy, reducing the mean pixel misclassification rate for an electron density phantom from 87% to 6%. The method is also applied to radiotherapy patient data, demonstrating how to derive the external field prior in a clinical context.     

Automatic image segmentation using constraint learning and propagation

In this paper, we propose automatic image segmentation using constraint learning and propagation. Recently, kernel learning is receiving much attention because a learned kernel can fit the given data better than a predefined kernel. To effectively learn the constraints generated by initial seeds for image segmentation, we employ kernel propagation (KP) based on kernel learning. The key idea of KP is first to learn a small-sized seed-kernel matrix and then propagate it into a large-sized full-kernel matrix. By applying KP to automatic image segmentation, we design a novel segmentation method to achieve high performance. First, we generate pairwise constraints, i.e., must-link and cannot-link, from initially selected seeds to make the seed-kernel matrix. To select the optimal initial seeds, we utilize global k-means clustering (GKM) and self-tuning spectral clustering (SSC). Next, we propagate the seed-kernel matrix into the full-kernel matrix of the entire image, and thus image segmentation results are obtained. We test our method on the Berkeley segmentation database, and the experimental results demonstrate that the proposed method is very effective in automatic image segmentation.     

Color clustering and learning for image segmentation based on neural networks

An image segmentation system is proposed for the segmentation of color image based on neural networks. In order to measure the color difference properly, image colors are represented in a modified L/sup */u/sup */v/sup */ color space. The segmentation system comprises unsupervised segmentation and supervised segmentation. The unsupervised segmentation is achieved by a two-level approach, i.e., color reduction and color clustering. In color reduction, image colors are projected into a small set of prototypes using self-organizing map (SOM) learning. In color clustering, simulated annealing (SA) seeks the optimal clusters from SOM prototypes. This two-level approach takes the advantages of SOM and SA, which can achieve the near-optimal segmentation with a low computational cost. The supervised segmentation involves color learning and pixel classification. In color learning, color prototype is defined to represent a spherical region in color space. A procedure of hierarchical prototype learning (HPL) is used to generate the different sizes of color prototypes from the sample of object colors. These color prototypes provide a good estimate for object colors. The image pixels are classified by the matching of color prototypes. The experimental results show that the system has the desired ability for the segmentation of color image in a variety of vision tasks.     

Lightweight deep learning methods for panoramic dental X-ray image segmentation

Neural Computing and Applications - Dental X-ray image segmentation is helpful for assisting clinicians to examine tooth conditions and identify dental diseases. Fast and lightweight segmentation...     

A multiple instance learning based framework for semantic image segmentation

Most image segmentation algorithms extract regions satisfying visual uniformity criteria. Unfortunately, because of the semantic gap between low-level features and high-level semantics, such regions usually do not correspond to meaningful parts. This has motivated researchers to develop methods that, by introducing high-level knowledge into the segmentation process, can break through the performance ceiling imposed by the semantic gap. The main disadvantage of those methods is their lack of flexibility due to the assumption that such knowledge is provided in advance. In content-based image retrieval (CBIR), relevance feedback (RF) learning has been successfully applied as a technique aimed at reducing the semantic gap. Inspired by this, we present a RF-based CBIR framework that uses multiple instance learning to perform a semantically-guided context adaptation of segmentation parameters. A partial instantiation of this framework that uses mean shift-based segmentation is presented. Experiments show the effectiveness and flexibility of the proposed framework on real images.     

深度学习背景下的图像语义分割方法综述

语义分割任务是很多计算机视觉任务的前提与基础,在虚拟现实、无人驾驶等领域具有重要的应用价值。随着深度学习技术的快速发展,尤其是卷积神经网络（convolutional neural network,CNN）的出现,使得图像语义分割取得了长足的进步。首先,本文介绍了语义分割概念、相关背景和语义分割基本处理流程。然后,总结开源的2D、2.5D、3D数据集和其相适应的分割方法,详细描述了不同网络的分割特点、优缺点及分割精确度,得出监督学习是有效的训练方式。同时,介绍了权威的算法性能评价指标,根据不同方法的侧重点,对各个分割方法的相关实验进行了对比分析,指出了目前实验方面整体存在的问题,其中,DeepLab-V3+网络在分割精确度和速度方面都具有良好的性能,应用价值较高。在此基础上,本文针对国内外的研究现状,提出了当前面临的几点挑战和未来可能的研究方向。通过总结与分析,能够为相关研究人员进行图像语义分割相关研究提供参考。     

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。     

Deep learning techniques for infrared image/video understanding

Multimedia Tools and Applications -     

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.     

A hybrid bio-inspired learning algorithm for image segmentation using multilevel thresholding

Multimedia Tools and Applications - In the field of image analysis, segmentation is one of the most important preprocessing steps. One way to achieve segmentation is the use of threshold selection,...