The non-rigid registration between CT data and ultrasonic images of liver can facilitate the diagnosis and treatment, which has been widely studied in recent years. To improve the registration accuracy of the Demons model on the non-rigid registration between 3D CT liver data and 2D ultrasonic images, a novel boundary extraction and enhancement method based on radial directional local intuitionistic fuzzy entropy in the polar coordinates has been put forward, and a new registration workflow has been provided. Experiments show that our method can acquire high-accuracy registration results. Experiments also show that the accuracy of the results of our method is higher than that of the original Demons method and the Demons method using simulated ultrasonic image by Field II. The operation time of our registration workflow is about 30 seconds, and it can be used in the surgery.
University of Zagreb, Faculty of Electrical Engineering and Computing（萨格勒布大学电气工程与计算学院）；Institute for Nuclear Technologies (INETEC)（核技术研究所（INETEC））
Non-destructive testing is a set of techniques for defect detection in materials. While the set of imaging techniques are manifold, ultrasonic imaging is the one used the most. The analysis is mainly performed by human inspectors manually analyzing recorded images. The low number of defects in real ultrasonic inspections and legal issues considering data from such inspections make it difficult to obtain proper results from automatic ultrasonic image (B-scan) analysis. In this paper, we present a novel deep learning Generative Adversarial Network model for generating ultrasonic B-scans with def…
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）
Lesion synthesis received much attention with the rise of efficient generative models for augmenting training data, drawing lesion evolution scenarios, or aiding expert training. The quality and diversity of synthesized data are highly dependent on the annotated data used to train the models, which not rarely struggle to derive very different yet realistic samples from the training ones. That adds an inherent bias to lesion segmentation algorithms and limits synthesizing lesion evolution scenarios efficiently. This paper presents a method for decoupling shape and density for liver lesion synth…
PLUS: Plug-and-Play Enhanced Liver Lesion Diagnosis Model on Non-Contrast CT Scans
School of Biomedical Engineering, Tsinghua University（清华大学生物医学工程学院）；DAMO Academy, Alibaba Group（阿里集团达摩院）；Hupan Lab（汇看实验室）；Department of Radiology, Shengjing Hospital of China Medical University（中国医科大学附属盛京医院放射科）；Department of Hepatobiliary and Pancreatic Surgery, First Affiliated Hospital of Zhejiang University（浙江大学医学院附属第一医院肝胆外科）
Hunan University of Technology and Business（湖南工商大学）；Utah State University（犹他州立大学）；University of Idaho（爱达荷大学）
Liver segmentation from abdominal CT images is an essential step for liver cancer computer-aided diagnosis and surgical planning. However, both the accuracy and robustness of existing liver segmentation methods cannot meet the requirements of clinical applications. In particular, for the common clinical cases where the liver tissue contains major pathology, current segmentation methods show poor performance. In this paper, we propose a novel low-rank tensor decomposition (LRTD) based multi-atlas segmentation (MAS) framework that achieves accurate and robust pathological liver segmentation of C…
Stanford University（斯坦福大学）；Varian Medical Systems（瓦里安医疗系统公司）；Huiyihuiying Medical Co（慧医慧影医疗公司）；Google Inc（谷歌公司）
Segmentation of livers and liver tumors is one of the most important steps in radiation therapy of hepatocellular carcinoma. The segmentation task is often done manually, making it tedious, labor intensive, and subject to intra-/inter- operator variations. While various algorithms for delineating organ-at-risks (OARs) and tumor targets have been proposed, automatic segmentation of livers and liver tumors remains intractable due to their low tissue contrast with respect to the surrounding organs and their deformable shape in CT images. The U-Net has gained increasing popularity recently for ima…
Liver segmentation on images acquired using computed tomography (CT) and magnetic resonance imaging (MRI) plays an important role in clinical management of liver diseases. Compared to MRI, CT images of liver are more abundant and readily available. However, MRI can provide richer quantitative information of the liver compared to CT. Thus, it is desirable to achieve unsupervised domain adaptation for transferring the learned knowledge from the source domain containing labeled CT images to the target domain containing unlabeled MR images. In this work, we report a novel unsupervised domain adapt…
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…
Diff4MMLiTS: Advanced Multimodal Liver Tumor Segmentation via Diffusion-Based Image Synthesis and Alignment
Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China（电子与电气工程系，南方科技大学，深圳，中国）；Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China（电气与电子工程系，香港大学，香港特别行政区，中国）；Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China（放射科，中大医院，医学院，东南大学，南京，中国）；Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China（嘉兴研究所，南方科技大学，嘉兴，中国）
PAII Inc.（PAII公司）；Chang Gung Memorial Hospital（长庚纪念医院）；Vanderbilt University（范德堡大学）；PingAn Technology（平安科技）
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…
Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease
Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria（维也纳医科大学生物医学影像与影像引导治疗部门）；Institute of Clinical Epidemiology, Public Health, Health Economics, Medical Statistics and Informatics, Medical University of Innsbruck, Austria（因斯布鲁克医科大学临床流行病学、公共卫生、卫生经济学、医学统计与信息学研究所）；Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA（诺华制药公司，东汉诺威，新泽西州，美国）；Novartis Pharma AG, Basel, Switzerland（诺华制药集团，巴塞尔，瑞士）；Comprehensive Center for Artificial Intelligence in Medicine, Medical University of Vienna, Austria（医学人工智能综合中心，维也纳医科大学，奥地利）；Division of Gastroenterology and Hepatology, Department of Medicine III, Medical University of Vienna, Austria（消化内科与肝病科，医学第三部，维也纳医科大学，奥地利）；Medical University of Vienna, Waehringer Guertel 18–20, 1090 Vienna, Austria（维也纳医科大学，瓦尔希林格特尔18–20号，1090维也纳，奥地利）
Yale University（耶鲁大学）；PAII Inc.（PAII公司）；Chang Gung Memorial Hospital（长庚纪念医院）；PingAn Technology（平安科技）
As the demand for more descriptive machine learning models grows within medical imaging, bottlenecks due to data paucity will exacerbate. Thus, collecting enough large-scale data will require automated tools to harvest data/label pairs from messy and real-world datasets, such as hospital PACS. This is the focus of our work, where we present a principled data curation tool to extract multi-phase CT liver studies and identify each scan's phase from a real-world and heterogenous hospital PACS dataset. Emulating a typical deployment scenario, we first obtain a set of noisy labels from our institut…
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心图像科学研究所）；Department of Biomedical Engineering, Eindhoven University of Technology（埃因霍温理工大学生物医学工程系）
Despite the successes of deep learning techniques at detecting objects in medical images, false positive detections occur which may hinder an accurate diagnosis. We propose a technique to reduce false positive detections made by a neural network using an SVM classifier trained with features derived from the uncertainty map of the neural network prediction. We demonstrate the effectiveness of this method for the detection of liver lesions on a dataset of abdominal MR images. We find that the use of a dropout rate of 0.5 produces the least number of false positives in the neural network predicti…
Centrum Wiskunde & Informatica（荷兰国家数学与计算机科学研究中心）；Applus+ E&I Technology Centre（Applus+ E&I 技术中心）；Leiden University（莱顿大学）；Utrecht University（乌得勒支大学）；University College London（伦敦大学学院）
In many ultrasonic imaging systems, data acquisition and image formation are performed on separate computing devices. Data transmission is becoming a bottleneck, thus, efficient data compression is essential. Compression rates can be improved by considering the fact that many image formation methods rely on approximations of wave-matter interactions, and only use the corresponding part of the data. Tailored data compression could exploit this, but extracting the useful part of the data efficiently is not always trivial. In this work, we tackle this problem using deep neural networks, optimized…
Segmentation of the liver from 3D computer tomography (CT) images is one of the most frequently performed operations in medical image analysis. In the past decade, Deep Learning Models (DMs) have offered significant improvements over previous methods for liver segmentation. The success of DMs is usually owed to the user's expertise in deep learning as well as to intricate training procedures. The need for bespoke expertise limits the reproducibility of empirical studies involving DMs. Today's consensus is that an ensemble of DMs works better than the individual component DMs. In this study we …
Rongcheng College, Harbin University of Science and Technology（哈尔滨理工大学荣成学院）；Harbin University of Science and Technology（哈尔滨理工大学）；Hunan University of Technology and Business（湖南工商大学）
Background and objective: In this paper, a modified U-Net based framework is presented, which leverages techniques from Squeeze-and-Excitation (SE) block, Atrous Spatial Pyramid Pooling (ASPP) and residual learning for accurate and robust liver CT segmentation, and the effectiveness of the proposed method was tested on two public datasets LiTS17 and SLiver07. Methods: A new network architecture called SAR-U-Net was designed. Firstly, the SE block is introduced to adaptively extract image features after each convolution in the U-Net encoder, while suppressing irrelevant regions, and highlightin…
Centrum Wiskunde & Informatica（荷兰国家数学与计算机科学研究中心）；Applus+ E&I Technology Centre（Applus+ E&I 技术中心）；Leiden University（莱顿大学）；Utrecht University（乌得勒支大学）；University College London（伦敦大学学院）
Ultrasonic imaging algorithms used in many clinical and industrial applications consist of three steps: A data pre-processing, an image formation and an image post-processing step. For efficiency, image formation often relies on an approximation of the underlying wave physics. A prominent example is the Delay-And-Sum (DAS) algorithm used in reflectivity-based ultrasonic imaging. Recently, deep neural networks (DNNs) are being used for the data pre-processing and the image post-processing steps separately. In this work, we propose a novel deep learning architecture that integrates all three ste…
One-sided ultrasonic non-destructive evaluation (UNDE) is extensively used to characterize structures that need to be inspected and maintained from defects and flaws that could affect the performance of power plants, such as nuclear power plants. Most UNDE systems send acoustic pulses into the structure of interest, measure the received waveform and use an algorithm to reconstruct the quantity of interest. The most widely used algorithm in UNDE systems is the synthetic aperture focusing technique (SAFT) because it produces acceptable results in real time. A few regularized inversion techniques…
Electron density maps must be accurately estimated to achieve valid dose calculation in MR-only radiotherapy. The goal of this study is to assess whether two deep learning models, the conditional generative adversarial network (cGAN) and the cycle-consistent generative adversarial network (cycleGAN), can generate accurate abdominal synthetic CT (sCT) images from 0.35T MR images for MR-only liver radiotherapy. A retrospective study was performed using CT images and 0.35T MR images of 12 patients with liver (n=8) and non-liver abdominal (n=4) cancer. CT images were deformably registered to the c…
Deep Regression 2D-3D Ultrasound Registration for Liver Motion Correction in Focal Tumor Thermal Ablation
Robarts Research Institute（罗伯茨研究所）；Western University（西安大略大学）；School of Biomedical Engineering, Western University（西安大略大学生物医学工程学院）；Lawson Health Research Institute（劳森健康研究所）
