Ultrasound (US) is a critical modality for diagnosing liver fibrosis. Unfortunately, assessment is very subjective, motivating automated approaches. We introduce a principled deep convolutional neural network (CNN) workflow that incorporates several innovations. First, to avoid overfitting on non-relevant image features, we force the network to focus on a clinical region of interest (ROI), encompassing the liver parenchyma and upper border. Second, we introduce global heteroimage fusion (GHIF), which allows the CNN to fuse features from any arbitrary number of images in a study, increasing its versatility and flexibility. Finally, we use 'style'-based view-specific parameterization (VSP) to tailor the CNN processing for different viewpoints of the liver, while keeping the majority of parameters the same across views. Experiments on a dataset of 610 patient studies (6979 images) demonstrate that our pipeline can contribute roughly 7% and 22% improvements in partial area under the curve and recall at 90% precision, respectively, over conventional classifiers, validating our approach to this crucial problem.
Multi-modal Liver Segmentation and Fibrosis Staging Using Real-world MRI Images
Multiscale X-ray Imaging (MXI) Lab, Department of Mechanical Engineering, University College London（多尺度X射线成像实验室，机械工程系，伦敦大学学院）；Centre for Clinical Brain Sciences, University of Edinburgh（临床脑科学中心，爱丁堡大学）；MRC Weatherall Institute of Molecular Medicine, University of Oxford（MRC韦伯尔分子医学研究所，牛津大学）
Rutgers University（罗格斯大学）；Rutgers Robert Wood Johnson Medical School（罗格斯罗伯特·伍德·约翰逊医学院）
Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver diseases (CLD) which can progress to liver cancer. The severity and treatment of NAFLD is determined by NAFLD Activity Scores (NAS)and liver fibrosis stage, which are usually obtained from liver biopsy. However, biopsy is invasive in nature and involves risk of procedural complications. Current methods to predict the fibrosis and NAS scores from noninvasive CT images rely heavily on either a large annotated dataset or transfer learning using pretrained networks. However, the availability of a large anno…
Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳市大数据研究院）
Accurate analysis of the fibrosis stage plays very important roles in follow-up of patients with chronic hepatitis B infection. In this paper, a deep learning framework is presented for automatically liver fibrosis prediction. On contrary of previous works, our approach can take use of the information provided by multiple ultrasound images. An indicator-guided learning mechanism is further proposed to ease the training of the proposed model. This follows the workflow of clinical diagnosis and make the prediction procedure interpretable. To support the training, a dataset is well-collected whic…
Computer Science Dept. Rutgers University（罗格斯大学计算机科学系）；Division of Gastroenterology and Hepatology Rutgers Robert Wood Johnson Medical School（罗格斯罗伯特·伍德·约翰逊医学院胃肠病学和肝病学分部）
Non-Alcoholic Fatty Liver Disease (NAFLD) is becoming increasingly prevalent in the world population. Without diagnosis at the right time, NAFLD can lead to non-alcoholic steatohepatitis (NASH) and subsequent liver damage. The diagnosis and treatment of NAFLD depend on the NAFLD activity score (NAS) and the liver fibrosis stage, which are usually evaluated from liver biopsies by pathologists. In this work, we propose a novel method to automatically predict NAS score and fibrosis stage from CT data that is non-invasive and inexpensive to obtain compared with liver biopsy. We also present a meth…
Shenzhen Research Institute of Big Data, the Chinese University of Hong Kong（香港中文大学深圳大数据研究院）；Huazhong University of Science and Technology（华中科技大学）；Shenzhen Third People’s Hospital（深圳市第三人民医院）
With the development of radiomics, noninvasive diagnosis like ultrasound (US) imaging plays a very important role in automatic liver fibrosis diagnosis (ALFD). Due to the noisy data, expensive annotations of US images, the application of Artificial Intelligence (AI) assisting approaches encounters a bottleneck. Besides, the use of mono-modal US data limits the further improve of the classification results. In this work, we innovatively propose a multi-modal fusion network with active learning (MMFN-AL) for ALFD to exploit the information of multiple modalities, eliminate the noisy data and red…
University of Oxford（牛津大学）；University of Jordan（约旦大学）
Assessing tumor tissue heterogeneity via ultrasound has recently been suggested for predicting early response to treatment. The ultrasound backscattering characteristics can assist in better understanding the tumor texture by highlighting local concentration and spatial arrangement of tissue scatterers. However, it is challenging to quantify the various tissue heterogeneities ranging from fine-to-coarse of the echo envelope peaks in tumor texture. Local parametric fractal features extracted via maximum likelihood estimation from five well-known statistical model families are evaluated for the …
Università degli Studi di Genova（热那亚大学）；IRCCS San Martino Hospital（IRCCS圣马蒂诺医院）
Due to the need for quick and effective treatments for liver diseases, which are among the most common health problems in the world, staging fibrosis through non-invasive and economic methods has become of great importance. Taking inspiration from diagnostic laparoscopy, used in the past for hepatic diseases, in this paper ultrasound images of the liver are studied, focusing on a specific region of the organ where the Glisson's capsule is visible. In ultrasound images, the Glisson's capsule appears in the shape of a line which can be extracted via classical methods in literature. By making use…
Medical imaging is the most important tool for detecting complications in the inner body of medicine. Nowadays, with the development of image processing technology as well as changing the size of photos to higher resolution images in the field of digital medical imaging, there is an efficient and accurate system for segmenting this. Real-world images that for a variety of reasons have poor heterogeneity, noise and contrast are essential. Digital image segmentation in medicine is used for diagnostic and therapeutic analysis, which is very helpful for physicians. In this study, we aim at liver c…
James Cook University（詹姆斯·库克大学）；Mahidol University（玛希隆大学）；University of Bremen（不来梅大学）
Automatic segmentation of hepatic lesions in computed tomography (CT) images is a challenging task to perform due to heterogeneous, diffusive shape of tumors and complex background. To address the problem more and more researchers rely on assistance of deep convolutional neural networks (CNN) with 2D or 3D type architecture that have proven to be effective in a wide range of computer vision tasks, including medical image processing. In this technical report, we carry out research focused on more careful approach to the process of learning rather than on complex architecture of the CNN. We have…
Precise Liver Tumor Segmentation in CT Using a Hybrid Deep Learning-Radiomics Framework
School of Information Science & Engineering, Shandong Normal University（信息科学与工程学院，山东师范大学）；Tajik State University of Law, Business and Politics（塔吉克国立法律、商业与政治大学）
University of Liverpool（利物浦大学）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；Science and Technologies Facilities Council（科学与技术设施委员会）
Interstitial lung diseases are a large group of heterogeneous diseases characterized by different degrees of alveolitis and pulmonary fibrosis. Accurately diagnosing these diseases has significant guiding value for formulating treatment plans. Although previous work has produced impressive results in classifying interstitial lung diseases, there is still room for improving the accuracy of these techniques, mainly to enhance automated decision-making. In order to improve the classification precision, our study proposes a convolutional neural networks-based framework with auxiliary information. …
ETH Zurich（苏黎世联邦理工学院）；University Hospital and University of Zurich（苏黎世大学医院和苏黎世大学）；Uppsala University（乌普萨拉大学）
Fluorescence microscopy allows for a detailed inspection of cells, cellular networks, and anatomical landmarks by staining with a variety of carefully-selected markers visualized as color channels. Quantitative characterization of structures in acquired images often relies on automatic image analysis methods. Despite the success of deep learning methods in other vision applications, their potential for fluorescence image analysis remains underexploited. One reason lies in the considerable workload required to train accurate models, which are normally specific for a given combination of markers…
National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；University of the Basque Country UPV/EHU（巴斯克大学（UPV/EHU））；TECNALIA, Basque Research and Technology Alliance (BRTA)（巴斯克研究与技术联盟（BRTA）泰克纳利亚研究中心）；Technical University of Munich（慕尼黑工业大学）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；University of Alberta（阿尔伯塔大学）；The Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）；Royal Brompton Hospital（皇家布朗普顿医院）
Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branch…
Department of Computer Science, Vanderbilt University（范德堡大学计算机科学系）
Histopathology has played an essential role in cancer diagnosis. With the rapid advances in convolutional neural networks (CNN). Various CNN-based automated pathological image segmentation approaches have been developed in computer-assisted pathological image analysis. In the past few years, Transformer neural networks (Transformer) have shown the unique merit of capturing the global long-distance dependencies across the entire image as a new deep learning paradigm. Such merit is appealing for exploring spatially heterogeneous pathological images. However, there have been very few, if any, stu…
University of Toronto（多伦多大学）；Sunnybrook Research Institute（桑尼布鲁克研究所）
Quantitative medical image computing (radiomics) has been widely applied to build prediction models from medical images. However, overfitting is a significant issue in conventional radiomics, where a large number of radiomic features are directly used to train and test models that predict genotypes or clinical outcomes. In order to tackle this problem, we propose an unsupervised learning pipeline composed of an autoencoder for representation learning of radiomic features and a Gaussian mixture model based on minimum message length criterion for clustering. By incorporating probabilistic modeli…
