In this paper we propose a fully automatic 2-stage cascaded approach for segmentation of liver and its tumors in CT (Computed Tomography) images using densely connected fully convolutional neural network (DenseNet). We independently train liver and tumor segmentation models and cascade them for a combined segmentation of the liver and its tumor. The first stage involves segmentation of liver and the second stage uses the first stage's segmentation results for localization of liver and henceforth tumor segmentations inside liver region. The liver model was trained on the down-sampled axial slices $(256 \times 256)$, whereas for the tumor model no down-sampling of slices was done, but instead it was trained on the CT axial slices windowed at three different Hounsfield (HU) levels. On the test set our model achieved a global dice score of 0.923 and 0.625 on liver and tumor respectively. The computed tumor burden had an rmse of 0.044.
Automatic segmentation of liver and its tumors is an essential step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis and assessment of tumor response to treatment. MICCAI 2017 Liver Tumor Segmentation Challenge (LiTS) provides a common platform for comparing different automatic algorithms on contrast-enhanced abdominal CT images in tasks including 1) liver segmentation, 2) liver tumor segmentation, and 3) tumor burden estimation. We participate this challenge by developing a hierarchical framework based on deep fully convolutional-deconvolutiona…
Yokohama City University Medical Center（横滨市立大学医学中心）
We present a fully automatic method employing convolutional neural networks based on the 2D U-net architecture and random forest classifier to solve the automatic liver lesion segmentation problem of the ISBI 2017 Liver Tumor Segmentation Challenge (LiTS). In order to constrain the ROI in which the tumors could be located, a liver segmentation is performed first. For the organ segmentation, an ensemble of convolutional networks is trained to segment a liver using a set of 179 liver CT datasets from liver surgery planning. Inside of the liver ROI a neural network, trained using 127 challenge tr…
The Chinese University of Hong Kong（香港中文大学）；Imsight Medical Technology, Inc.（伊赛智能医疗科技有限公司）
Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2D and 3D FCNs, serve as the back-bone in many volumetric image segmentation. However, 2D convolutions can not fully leverage the spatial information along the third dimension while 3D convolutions suffer from high computational cost and GPU memory consumption. To address these issues, we …
Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. Recently, 2D and 3D deep convolutional neural networks have become popular in medical image segmentation tasks because of the utilization of large labeled datasets to learn hierarchical features. However, 3D networks have some drawbacks due to their high cost on computational resources. In this paper, we propose a 3D hybrid residual attention-aware segmentation method, named RA-UNet, to precisely extract the liver volume of interests (VOI) and segment tumors from the l…
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of Southern California（南加利福尼亚大学）；Lenovo Research（联想研究院）；Lenovo Ltd.（联想有限公司）
Purpose: Automated liver tumor segmentation from Computed Tomography (CT) images is a necessary prerequisite in the interventions of hepatic abnormalities and surgery planning. However, accurate liver tumor segmentation remains challenging due to the large variability of tumor sizes and inhomogeneous texture. Recent advances based on Fully Convolutional Network (FCN) for medical image segmentation drew on the success of learning discriminative pyramid features. In this paper, we propose a Decoupled Pyramid Correlation Network (DPC-Net) that exploits attention mechanisms to fully leverage both …
LIDIA: Precise Liver Tumor Diagnosis on Multi-Phase Contrast-Enhanced CT via Iterative Fusion and Asymmetric Contrastive Learning
College of Computer Science, Sichuan University（四川大学计算机学院）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Hupan Lab（湖畔实验室）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）；First Affiliated Hospital of Zhejiang University（浙江大学附属第一医院）
ASLseg: Adapting SAM in the Loop for Semi-supervised Liver Tumor Segmentation
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）
T-MPEDNet: Unveiling the Synergy of Transformer-aware Multiscale Progressive Encoder-Decoder Network with Feature Recalibration for Tumor and Liver Segmentation
Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Indore, Indore 453552, India（计算机科学与工程系，印度理工学院（IIT）印尔德，印尔德453552，印度）；Department of Electrical Engineering, Indian Institute of Technology (IIT) Indore, Indore 453552, India（电气工程系，印度理工学院（IIT）印尔德，印尔德453552，印度）
National Institutes of Health Clinical Center（美国国立卫生研究院临床中心）；Ping An Insurance Company of China（中国平安保险公司）
Developing an effective liver and liver tumor segmentation model from CT scans is very important for the success of liver cancer diagnosis, surgical planning and cancer treatment. In this work, we propose a two-stage framework for 2D liver and tumor segmentation. The first stage is a coarse liver segmentation network, while the second stage is an edge enhanced network (E$^2$Net) for more accurate liver and tumor segmentation. E$^2$Net explicitly models complementary objects (liver and tumor) and their edge information within the network to preserve the organ and lesion boundaries. We introduce…
Accurate visualization of liver tumors and their surrounding blood vessels is essential for noninvasive diagnosis and prognosis prediction of tumors. In medical image segmentation, there is still a lack of in-depth research on the simultaneous segmentation of liver tumors and peritumoral blood vessels. To this end, we collect the first liver tumor, and vessel segmentation benchmark datasets containing 52 portal vein phase computed tomography images with liver, liver tumor, and vessel annotations. In this case, we propose a 3D U-shaped Cross-Attention Network (UCA-Net) that utilizes a tailored …
China University of Petroleum（中国石油大学）；Qingdao Municipal Hospital（青岛市市立医院）；University of Chinese Academy of Sciences（中国科学院大学）
Liver cancer is one of the most common malignant diseases in the world. Segmentation and labeling of liver tumors and blood vessels in CT images can provide convenience for doctors in liver tumor diagnosis and surgical intervention. In the past decades, many state-of-the-art medical image segmentation algorithms appeared during this period. With the development of embedded devices, embedded deployment for medical segmentation and automatic reconstruction brings prospects for future automated surgical tasks. Yet, most of the existing segmentation methods mostly care about the spatial feature co…
Technical University of Munich（慕尼黑工业大学）；University of Zurich（苏黎世大学）；Ecole Polytechnique de Montréal（蒙特利尔理工学院）；Tel-Aviv University（特拉维夫大学）；Imperial College London（帝国理工学院）；Hebrew University of Jerusalem（希伯来大学）；Radboud University Medical Center（拉德堡德大学医学中心）；The University of Montréal Hospital Research Centre（蒙特利尔大学医院研究中心）；LMU Munich（慕尼黑大学）
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liv…
Johns Hopkins University（约翰斯·霍普金斯大学）；PAII Inc.（PAII公司）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）；Ping An Technology（平安科技）；Shanghai Jiao Tong University（上海交通大学）
The boundary of tumors (hepatocellular carcinoma, or HCC) contains rich semantics: capsular invasion, visibility, smoothness, folding and protuberance, etc. Capsular invasion on tumor boundary has proven to be clinically correlated with the prognostic indicator, microvascular invasion (MVI). Investigating tumor boundary semantics has tremendous clinical values. In this paper, we propose the first and novel computational framework that disentangles the task into two components: spatial vertex localization and sequential semantic classification. (1) A HCC tumor segmentor is built for tumor mask …
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Lenovo Research（联想研究院）
Liver tumor segmentation plays an important role in hepatocellular carcinoma diagnosis and surgical planning. In this paper, we propose a novel Semantic Feature Attention Network (SFAN) for liver tumor segmentation from Computed Tomography (CT) volumes, which exploits the impact of both low-level and high-level features. In the SFAN, a Semantic Attention Transmission (SAT) module is designed to select discriminative low-level localization details with the guidance of neighboring high-level semantic information. Furthermore, a Global Context Attention (GCA) module is proposed to effectively fus…
Washington State University（华盛顿州立大学）；M.D. Anderson Cancer Center, University of Texas（德克萨斯大学MD安德森癌症中心）
Tumor detection in biomedical imaging is a time-consuming process for medical professionals and is not without errors. Thus in recent decades, researchers have developed algorithmic techniques for image processing using a wide variety of mathematical methods, such as statistical modeling, variational techniques, and machine learning. In this paper, we propose a semi-automatic method for liver segmentation of 2D CT scans into three labels denoting healthy, vessel, or tumor tissue based on graph cuts. First, we create a feature vector for each pixel in a novel way that consists of the 59 intensi…
YOLO-PVC: 2D-to-3D Consolidation of Slice-wise Detections for Volumetric Liver Tumor Localization in MRI
ESME Research Lab（ESME研究实验室）；Université Paris-Est Créteil（巴黎东部克雷泰伊大学）；LRE EPITA（EPITA LRE实验室）；Institute of Space Technology（空间技术研究所）；Henri Mondor University Hospital（亨利·蒙多大学医院）
University of Innsbruck（因斯布鲁克大学）；Medical University of Innsbruck（因斯布鲁克医科大学）
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer in adults, and the most common cause of death of people suffering from cirrhosis. The segmentation of liver lesions in CT images allows assessment of tumor load, treatment planning, prognosis and monitoring of treatment response. Manual segmentation is a very time-consuming task and in many cases, prone to inaccuracies and automatic tools for tumor detection and segmentation are desirable. In this paper, we compare two network architectures, one that is composed of one neural network and manages the segmentation tas…
Automatic segmentation of liver tumors in medical images is crucial for the computer-aided diagnosis and therapy. It is a challenging task, since the tumors are notoriously small against the background voxels. This paper proposes a new three-stage curriculum learning approach for training deep networks to tackle this small object segmentation problem. The learning in the first stage is performed on the whole input to obtain an initial deep network for tumor segmenta-tion. Then the second stage of learning focuses the strength-ening of tumor specific features by continuing training the network …
Huazhong University of Science and Technology（华中科技大学）；The Chinese University of Hong Kong – Shenzhen（香港中文大学（深圳））；Johns Hopkins University（约翰斯·霍普金斯大学）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）
We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intriguing advantages: (I) realistic in shape and texture, which even medical professionals can confuse with real tumors; (II) effective for training AI models, which can perform liver tumor segmentation similarly to the model trained on real tumors -- this result is exciting because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to real tumors. This result also impl…
PAII Inc.（PAII 公司）；Vanderbilt University（范德堡大学）；University of Texas, Arlington（德克萨斯大学阿灵顿分校）；Chang Gung Memorial Hospital（长庚纪念医院）；PingAn Technology（平安科技）
Non-invasive radiological-based lesion characterization and identification, e.g., to differentiate cancer subtypes, has long been a major aim to enhance oncological diagnosis and treatment procedures. Here we study a specific population of human subjects, with the hope of reducing the need for invasive surgical biopsies of liver cancer patients, which can cause many harmful side-effects. To this end, we propose a fully-automated and multi-stage liver tumor characterization framework designed for dynamic contrast computed tomography (CT). Our system comprises four sequential processes of tumor …
Technical University of Munich（慕尼黑工业大学）；LMU Hospital Grosshadern（路德维希-马克西米利安-慕尼黑大学格罗斯哈德恩医院）
Automatic segmentation of the liver and hepatic lesions is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This paper presents a method to automatically segment liver and lesions in CT and MRI abdomen images using cascaded fully convolutional neural networks (CFCNs) enabling the segmentation of a large-scale medical trial or quantitative image analysis. We train and cascade two FCNs for a combined segmentation of the liver and its lesions. In the first step, we train a FCN to segment the liver as ROI input …
Ping An Technology（平安科技）；PAII Inc.（PAII公司）；Eastern Hepatobiliary Surgery Hospital（东方肝胆外科医院）；Vanderbilt University（范德堡大学）；Northwestern Polytechnical University（西北工业大学）
Using radiological scans to identify liver tumors is crucial for proper patient treatment. This is highly challenging, as top radiologists only achieve F1 scores of roughly 80% (hepatocellular carcinoma (HCC) vs. others) with only moderate inter-rater agreement, even when using multi-phase magnetic resonance (MR) imagery. Thus, there is great impetus for computer-aided diagnosis (CAD) solutions. A critical challenge is to robustly parse a 3D MR volume to localize diagnosable regions of interest (ROI), especially for edge cases. In this paper, we break down this problem using a key-slice parser…
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…
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…
Shandong Normal University（山东师范大学）；Western University（韦仕敦大学）
Simultaneous segmentation and detection of liver tumors (hemangioma and hepatocellular carcinoma (HCC)) by using multi-modality non-contrast magnetic resonance imaging (NCMRI) are crucial for the clinical diagnosis. However, it is still a challenging task due to: (1) the HCC information on NCMRI is invisible or insufficient makes extraction of liver tumors feature difficult; (2) diverse imaging characteristics in multi-modality NCMRI causes feature fusion and selection difficult; (3) no specific information between hemangioma and HCC on NCMRI cause liver tumors detection difficult. In this stu…
Trustworthy Multi-phase Liver Tumor Segmentation via Evidence-based Uncertainty
Southeast University（东南大学）；the First Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第一医院）；Zhongda Hospital, School of Medicine, Southeast University（东南大学医学院附属中大医院）
Parameter-Efficient Fine-Tuning of Foundation Models for Liver Tumor Segmentation in CT
University Health Network（大学健康网络）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；University of Alberta（阿尔伯塔大学）；Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）；Islamic Azad University（伊斯兰阿扎德大学）
Zhejiang University（浙江大学）；University of Science and Technology of China（中国科学技术大学）；Zhejiang Lab（之江实验室）；Ritsumeikan University（立命馆大学）；Sir Run Run Shaw Hospital（邵逸夫医院）；The First Affiliated Hospital, College of Medicine, Zhejiang University（浙江大学医学院附属第一医院）
Multi-phase computed tomography (CT) images provide crucial complementary information for accurate liver tumor segmentation (LiTS). State-of-the-art multi-phase LiTS methods usually fused cross-phase features through phase-weighted summation or channel-attention based concatenation. However, these methods ignored the spatial (pixel-wise) relationships between different phases, hence leading to insufficient feature integration. In addition, the performance of existing methods remains subject to the uncertainty in segmentation, which is particularly acute in tumor boundary regions. In this work,…
Anisotropic Hybrid Networks for liver tumor segmentation with uncertainty quantification
Univ. Grenoble Alpes（格勒诺布尔大学）；Inserm（法国国家健康与医学研究院）；Grenoble Institut Neurosciences（格勒诺布尔神经科学研究所）；Pixyl（Pixyl公司）；Research and Development Laboratory（研发实验室）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔综合理工学院）；LJK（格勒诺布尔大学联合实验室（注：因LJK无明确对应中文通用名称，按原文保留英文缩写））
Mansoura University（曼苏尔大学）；South Valley University（南河谷大学）；Higher Institute of Engineering and Technology（高等工程技术学院）
The liver is one of the most critical metabolic organs in vertebrates due to its vital functions in the human body, such as detoxification of the blood from waste products and medications. Liver diseases due to liver tumors are one of the most common mortality reasons around the globe. Hence, detecting liver tumors in the early stages of tumor development is highly required as a critical part of medical treatment. Many imaging modalities can be used as aiding tools to detect liver tumors. Computed tomography (CT) is the most used imaging modality for soft tissue organs such as the liver. This …
In this paper, we propose a bottleneck supervised (BS) U-Net model for liver and tumor segmentation. Our main contributions are: first, we propose a variation of the original U-Net that incorporates dense modules, inception modules and dilated convolution in the encoding path; second, we propose a bottleneck supervised (BS) U-Net that contains an encoding U-Net and a segmentation U-Net. To train the BS U-Net, the encoding U-Net is first trained to get encodings of the label maps that contain the anatomical information (shape and location). Subsequently, this information is used to guide the tr…
Shanghai University（上海大学）；Shanghai Jiao Tong University（上海交通大学）；University of Chinese Academy of Sciences（中国科学院大学）；Northeastern University（东北大学）
Liver tumor segmentation in CT images is a critical step in the diagnosis, surgical planning and postoperative evaluation of liver disease. An automatic liver and tumor segmentation method can greatly relieve physicians of the heavy workload of examining CT images and better improve the accuracy of diagnosis. In the last few decades, many modifications based on U-Net model have been proposed in the literature. However, there are relatively few improvements for the advanced UNet++ model. In our paper, we propose an encoding feature supervised UNet++(ES-UNet++) and apply it to the liver and tumo…
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）
TU Graz, Institute for Computer Graphics and Vision（格拉茨技术大学计算机图形与视觉研究所）；BioTechMed（生物技术医学研究联盟）；Shanghai Jiao Tong University, School of Mechanical Engineering（上海交通大学机械工程学院）；Katharinenhospital（卡塔琳娜医院）
Ultrasound (US) is the most commonly used liver imaging modality worldwide. It plays an important role in follow-up of cancer patients with liver metastases. We present an interactive segmentation approach for liver tumors in US acquisitions. Due to the low image quality and the low contrast between the tumors and the surrounding tissue in US images, the segmentation is very challenging. Thus, the clinical practice still relies on manual measurement and outlining of the tumors in the US images. We target this problem by applying an interactive segmentation algorithm to the US data, allowing th…
In this paper we present a new method for motion tracking of tumors in liver ultrasound image sequences. Our algorithm has two main steps. In the first step, we apply mean shift algorithm with multiple features to estimate the center of the target in each frame. Target in the first frame is defined using an ellipse. Edge, texture, and intensity features are extracted from the first frame, and then mean shift algorithm is applied to each feature separately to find the center of ellipse related to that feature in the next frame. The center of ellipse will be the weighted average of these centers…
A Multi-Stage Deep Learning Framework with PKCP-MixUp Augmentation for Pediatric Liver Tumor Diagnosis Using Multi-Phase Contrast-Enhanced CT
Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院武汉儿童医院（武汉市妇幼保健院））；Wuhan University（武汉大学）；Wuhan Cloud Computing Technology Co., Ltd.（武汉云计算技术有限公司）
Enhanced Liver Tumor Detection in CT Images Using 3D U-Net and Bat Algorithm for Hyperparameter Optimization
Department of Mathematics and Statistics, Loyola University Chicago（洛约拉大学芝加哥分校数学与统计学系）；Department of Mathematics and Computer Science, Islamic Azad University, Science and Research Branch（伊斯兰 Azad 大学科学与研究分校数学与计算机科学系）
本文提出将 3D U-Net 架构与 Bat Algorithm 结合，通过优化学习率和批大小等超参数实现 CT 图像中肝肿瘤的自动分割，有效提升了分割精度与鲁棒性。
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Lenovo Research（联想研究院）；Lenovo Ltd.（联想集团有限公司）
Liver cancer is one of the most common cancers worldwide. Due to inconspicuous texture changes of liver tumor, contrast-enhanced computed tomography (CT) imaging is effective for the diagnosis of liver cancer. In this paper, we focus on improving automated liver tumor segmentation by integrating multi-modal CT images. To this end, we propose a novel mutual learning (ML) strategy for effective and robust multi-modal liver tumor segmentation. Different from existing multi-modal methods that fuse information from different modalities by a single model, with ML, an ensemble of modality-specific mo…
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 …
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）
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…
Live(r) Die: Predicting Survival in Colorectal Liver Metastasis
Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada（多伦多大学医学生物物理学系）；Department of Medical Imaging, University of Toronto, Toronto, ON, Canada（多伦多大学医学影像学系）；Sunnybrook Research Institute, Toronto, ON, Canada（辛普森研究学院）
Sun Yat-sen University（中山大学）；The First Affiliated Hospital, Sun Yat-sen University（中山大学附属第一医院）；Infervision Inc.（推想医疗科技股份有限公司）
Hepatocellular carcinoma (HCC) is the second most frequent cause of malignancy-related death and is one of the diseases with the highest incidence in the world. Because the liver is the only organ in the human body that is supplied by two major vessels: the hepatic artery and the portal vein, various types of malignant tumors can spread from other organs to the liver. And due to the liver masses' heterogeneous and diffusive shape, the tumor lesions are very difficult to be recognized, thus automatic lesion detection is necessary for the doctors with huge workloads. To assist doctors, this work…
Parameter-Efficient 3D Segmentation of Liver and Liver tumors: Depthwise factorization Scales Better Than Dense Convolution with Spatial Dimensionality
Accurate liver and lesion segmentation from computed tomography (CT) images are highly demanded in clinical practice for assisting the diagnosis and assessment of hepatic tumor disease. However, automatic liver and lesion segmentation from contrast-enhanced CT volumes is extremely challenging due to the diversity in contrast, resolution, and quality of images. Previous methods based on UNet for 2D slice-by-slice or 3D volume-by-volume segmentation either lack sufficient spatial contexts or suffer from high GPU computational cost, which limits the performance. To tackle these issues, we propose…
Comparative Study of UNet-based Architectures for Liver Tumor Segmentation in Multi-Phase Contrast-Enhanced Computed Tomography
School of Computer Science and Engineering（计算机科学与工程学院）；The Saigon International University（暹罗国际大学）；School of Biomedical Engineering（生物医学工程学院）；International University（国际大学）；Vietnam National University HCMC（越南国家大学胡志明市分校）
We propose a computationally efficient architecture that learns to segment lesions from CT images of the liver. The proposed architecture uses bilinear interpolation with sub-pixel convolution at the last layer to upscale the course feature in bottle neck architecture. Since bilinear interpolation and sub-pixel convolution do not have any learnable parameter, our overall model is faster and occupies less memory footprint than the traditional U-net. We evaluate our proposed architecture on the highly competitive dataset of 2017 Liver Tumor Segmentation (LiTS) Challenge. Our method achieves comp…
University of Jordan（约旦大学）；King Abdullah II School for IT（阿卜杜拉二世信息技术学院）
Effective ultrasound tissue characterization is usually hindered by complex tissue structures. The interlacing of speckle patterns complicates the correct estimation of backscatter distribution parameters. Nakagami parametric imaging based on localized shape parameter mapping can model different backscattering conditions. However, performance of the constructed Nakagami image depends on the sensitivity of the estimation method to the backscattered statistics and scale of analysis. Using a fixed focal region of interest in estimating the Nakagami parametric image would increase estimation varia…
Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局资讯通信研究院）；National University of Singapore（新加坡国立大学）；Singapore General Hospital（新加坡中央医院）；Duke-NUS Medical School Singapore（新加坡杜克-新加坡国立大学医学院）
Accurate automatic liver and tumor segmentation plays a vital role in treatment planning and disease monitoring. Recently, deep convolutional neural network (DCNNs) has obtained tremendous success in 2D and 3D medical image segmentation. However, 2D DCNNs cannot fully leverage the inter-slice information, while 3D DCNNs are computationally expensive and memory intensive. To address these issues, we first propose a novel dense-sparse training flow from a data perspective, in which, densely adjacent slices and sparsely adjacent slices are extracted as inputs for regularizing DCNNs, thereby impro…
