U-Net has achieved huge success in various medical image segmentation challenges. Kinds of new architectures with bells and whistles might succeed in certain dataset when employed with optimal hyper-parameter, but their generalization always can't be guaranteed. Here, we focused on the basic U-Net architecture and proposed a multi scale supervised 3D U-Net for the segmentation task in KiTS19 challenge. To enhance the performance, our work can be summarized as three folds: first, we used multi scale supervision in the decoder pathway, which could encourage the network to predict right results from the deep layers; second, with the aim to alleviate the bad effect from the sample imbalance of kidney and tumor, we adopted exponential logarithmic loss; third, a connected-component based post processing method was designed to remove the obviously wrong voxels. In the published KiTS19 training dataset (totally 210 patients), we divided 42 patients to be test dataset and finally obtained DICE scores of 0.969 and 0.805 for the kidney and tumor respectively. In the challenge, we finally achieved the 7th place among 106 teams with the Composite Dice of 0.8961, namely 0.9741 for kidney and 0.8181 for tumor.
NVIDIA（英伟达）；University of California, Los Angeles（加州大学洛杉矶分校）
Automated segmentation of kidneys and kidney tumors is an important step in quantifying the tumor's morphometrical details to monitor the progression of the disease and accurately compare decisions regarding the kidney tumor treatment. Manual delineation techniques are often tedious, error-prone and require expert knowledge for creating unambiguous representation of kidneys and kidney tumors segmentation. In this work, we propose an end-to-end boundary aware fully Convolutional Neural Networks (CNNs) for reliable kidney and kidney tumor semantic segmentation from arterial phase abdominal 3D CT…
Couger Inc（库格公司）；Sethu Institute of Technology（塞图理工学院）；Thiagarajar college of Engineering（蒂亚加拉贾工程学院）
KiTs19 challenge paves the way to haste the improvement of solid kidney tumor semantic segmentation methodologies. Accurate segmentation of kidney tumor in computer tomography (CT) images is a challenging task due to the non-uniform motion, similar appearance and various shape. Inspired by this fact, in this manuscript, we present a novel kidney tumor segmentation method using deep learning network termed as Hyper vision Net model. All the existing U-net models are using a modified version of U-net to segment the kidney tumor region. In the proposed architecture, we introduced supervision laye…
University of Minnesota（明尼苏达大学）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；University of Heidelberg（海德堡大学）；PingAn Technology Co., Ltd（平安科技有限公司）；Shanghai United Imaging Intelligence Inc.（上海联影智能医疗科技有限公司）；Southern Medical University（南方医科大学）；Peking University First Hospital（北京大学第一医院）；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）
There is a large body of literature linking anatomic and geometric characteristics of kidney tumors to perioperative and oncologic outcomes. Semantic segmentation of these tumors and their host kidneys is a promising tool for quantitatively characterizing these lesions, but its adoption is limited due to the manual effort required to produce high-quality 3D segmentations of these structures. Recently, methods based on deep learning have shown excellent results in automatic 3D segmentation, but they require large datasets for training, and there remains little consensus on which methods perform…
Nanjing University（南京大学）；Medical School of Nanjing University（南京大学医学院）；National Institute of Healthcare Data Science, Nanjing University（南京大学国家健康数据科学研究院）；UNC Chapel Hill（北卡罗来纳大学教堂山分校）
Conventional data augmentation realized by performing simple pre-processing operations (\eg, rotation, crop, \etc) has been validated for its advantage in enhancing the performance for medical image segmentation. However, the data generated by these conventional augmentation methods are random and sometimes harmful to the subsequent segmentation. In this paper, we developed a novel automatic learning-based data augmentation method for medical image segmentation which models the augmentation task as a trial-and-error procedure using deep reinforcement learning (DRL). In our method, we innovativ…
The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT
University of Minnesota（明尼苏达大学）；German Cancer Research Center（德国癌症研究中心）；Shanghai Jiao Tong University（上海交通大学）；IBM Research（IBM研究院）；Monash University（莫纳什大学）；Northwestern Polytechnical University（西北工业大学）；The University of Sydney（悉尼大学）；Nankai University（南开大学）
College of Biomedical Engineering, Rangsit University（兰实大学生物医学工程学院）
Automated medical image segmentation is a priority research area for computational methods. In particular, detection of cancerous tumors represents a current challenge in this area with potential for real-world impact. This paper describes a method developed in response to the 2019 Kidney Tumor Segmentation Challenge (KiTS19). Axial computed tomography (CT) scans from 210 kidney cancer patients were used to develop and evaluate this automatic segmentation method based on a logical ensemble of fully-convolutional network (FCN) architectures, followed by volumetric validation. Data was pre-proce…
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）；Southeast University（东南大学）；Lenovo Research（联想研究院）
Automated segmentation of kidney and tumor from 3D CT scans is necessary for the diagnosis, monitoring, and treatment planning of the disease. In this paper, we describe a two-stage framework for kidney and tumor segmentation based on 3D fully convolutional network (FCN). The first stage preliminarily locate the kidney and cut off the irrelevant background to reduce class imbalance and computation cost. Then the second stage precisely segment the kidney and tumor on the cropped patch. The proposed method ranks the 4th place out of 105 competitive teams in MICCAI 2019 KiTS Challenge with a Comp…
Image Sciences Institute, UMC Utrecht, Utrecht University（乌得勒支大学医学中心影像科学研究所）
This paper assesses whether using clinical characteristics in addition to imaging can improve automated segmentation of kidney cancer on contrast-enhanced computed tomography (CT). A total of 300 kidney cancer patients with contrast-enhanced CT scans and clinical characteristics were included. A baseline segmentation of the kidney cancer was performed using a 3D U-Net. Input to the U-Net were the contrast-enhanced CT images, output were segmentations of kidney, kidney tumors, and kidney cysts. A cognizant sampling strategy was used to leverage clinical characteristics for improved segmentation…
Umeå University（于默奥大学）；Eindhoven University of Technology（埃因霍温理工大学）
Kidney tumor segmentation emerges as a new frontier of computer vision in medical imaging. This is partly due to its challenging manual annotation and great medical impact. Within the scope of the Kidney Tumor Segmentation Challenge 2019, that is aiming at combined kidney and tumor segmentation, this work proposes a novel combination of 3D U-Nets---collectively denoted TuNet---utilizing the resulting kidney masks for the consecutive tumor segmentation. The proposed method achieves a Sørensen-Dice coefficient score of 0.902 for the kidney, and 0.408 for the tumor segmentation, computed from a f…
ARTORG Center for Biomedical Engineering Research, University of Bern（伯尔尼大学生物医学工程研究中心）
Each year, there are about 400'000 new cases of kidney cancer worldwide causing around 175'000 deaths. For clinical decision making it is important to understand the morphometry of the tumor, which involves the time-consuming task of delineating tumor and kidney in 3D CT images. Automatic segmentation could be an important tool for clinicians and researchers to also study the correlations between tumor morphometry and clinical outcomes. We present a segmentation method which combines the popular U-Net convolutional neural network architecture with post-processing based on statistical constrain…
Precise characterization of the kidney and kidney tumor characteristics is of outmost importance in the context of kidney cancer treatment, especially for nephron sparing surgery which requires a precise localization of the tissues to be removed. The need for accurate and automatic delineation tools is at the origin of the KiTS19 challenge. It aims at accelerating the research and development in this field to aid prognosis and treatment planning by providing a characterized dataset of 300 CT scans to be segmented. To address the challenge, we proposed an automatic, multi-stage, 2.5D deep learn…
Accurate segmentation of kidneys and kidney tumors is an essential step for radiomic analysis as well as developing advanced surgical planning techniques. In clinical analysis, the segmentation is currently performed by clinicians from the visual inspection images gathered through a computed tomography (CT) scan. This process is laborious and its success significantly depends on previous experience. Moreover, the uncertainty in the tumor location and heterogeneity of scans across patients increases the error rate. To tackle this issue, computer-aided segmentation based on deep learning techniq…
Hybrid Deep Learning Framework for Classification of Kidney CT Images: Diagnosis of Stones, Cysts, and Tumors
School of Engineering & Technology, BML Munjal University（BML Munjal大学工程与技术学院）；Center for Advanced Data and Computational Science, BML Munjal University（BML Munjal大学先进数据与计算科学中心）
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…
Breast tumor segmentation provides accurate tumor boundary, and serves as a key step toward further cancer quantification. Although deep learning-based approaches have been proposed and achieved promising results, existing approaches have difficulty in detecting small breast tumors. The capacity to detecting small tumors is particularly important in finding early stage cancers using computer-aided diagnosis (CAD) systems. In this paper, we propose a novel deep learning architecture called Small Tumor-Aware Network (STAN), to improve the performance of segmenting tumors with different size. The…
University of Idaho（爱达荷大学）；University of Utah School of Medicine（犹他大学医学院）
Breast tumor segmentation is a critical task in computer-aided diagnosis (CAD) systems for breast cancer detection because accurate tumor size, shape and location are important for further tumor quantification and classification. However, segmenting small tumors in ultrasound images is challenging, due to the speckle noise, varying tumor shapes and sizes among patients, and the existence of tumor-like image regions. Recently, deep learning-based approaches have achieved great success for biomedical image analysis, but current state-of-the-art approaches achieve poor performance for segmenting …
Glioma, the malignant brain tumor, requires immediate treatment to improve the survival of patients. Gliomas heterogeneous nature makes the segmentation difficult, especially for sub-regions like necrosis, enhancing tumor, non-enhancing tumor, and Edema. Deep neural networks like full convolution neural networks and ensemble of fully convolution neural networks are successful for Glioma segmentation. The paper demonstrates the use of a 3D fully convolution neural network with a three layer encoder decoder approach for layer arrangement. The encoder blocks include the dense modules, and decoder…
Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images
Florida Atlantic University（佛罗里达大西洋大学）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学）；Missouri University of Science and Technology（密苏里科技大学）；Florida International University（佛罗里达国际大学）
Kidney structures segmentation is a crucial yet challenging task in the computer-aided diagnosis of surgery-based renal cancer. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of kidney structures on computed tomography angiography (CTA) images remains challenging, due to the variable sizes of kidney tumors and the ambiguous boundaries between kidney structures and their surroundings. In this paper, we propose a boundary-aware network (BA-Net) to segment kidneys, kidney tumors, arteries, and veins on CTA sc…
University of Maryland, College Park（马里兰大学帕克分校）；Intuitive Surgical, Inc（直观外科公司）
Semantic image segmentation plays an important role in modeling patient-specific anatomy. We propose a convolution neural network, called Kid-Net, along with a training schema to segment kidney vessels: artery, vein and collecting system. Such segmentation is vital during the surgical planning phase in which medical decisions are made before surgical incision. Our main contribution is developing a training schema that handles unbalanced data, reduces false positives and enables high-resolution segmentation with a limited memory budget. These objectives are attained using dynamic weighting, ran…
Robust Renal Mass Segmentation on CT: A Validation Study of an AI-Based Framework
Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands（医学影像部门，Radboudumc，尼姆维根，荷兰）；Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany（放射科，Charité - 大学医学中心柏林，柏林，德国）；Department of Neuroradiology, Charité - Universitätsmedizin Berlin, Berlin, Germany（神经放射科，Charité - 大学医学中心柏林，柏林，德国）；Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany（诊断和介入放射科，Klinikum rechts der Isar，TUM大学医院，慕尼黑技术大学，慕尼黑，德国）；Department of Cardiovascular Radiology and Nuclear Medicine, German Heart Center, TUM University Hospital, Technical University of Munich, Munich, Germany（心血管放射学和核医学部，德国心脏中心，TUM大学医院，慕尼黑技术大学，慕尼黑，德国）；Fraunhofer MEVIS, Bremen, Germany（Fraunhofer MEVIS，不莱梅，德国）
Technical University of Kosice（科希策工业大学）；Pavol Jozef Safarik University（帕沃尔·约瑟夫·沙法里克大学）；Louis Pasteur University Hospital（路易斯·巴斯德大学医院）
The kidney cancer is one of the most common cancer types. The treatment frequently include surgical intervention. However, surgery is in this case particularly challenging due to regional anatomical relations. Organ delineation can significantly improve surgical planning and execution. In this contribution, we propose ensemble of two fully convolutional networks for segmentation of kidney, tumor, veins and arteries. While SegResNet architecture achieved better performance on tumor, the nnU-Net provided more precise segmentation for kidneys, arteries and veins. So in our proposed approach we co…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Image segmentation has been increasingly applied in medical settings as recent developments have skyrocketed the potential applications of deep learning. Urology, specifically, is one field of medicine that is primed for the adoption of a real-time image segmentation system with the long-term aim of automating endoscopic stone treatment. In this project, we explored supervised deep learning models to annotate kidney stones in surgical endoscopic video feeds. In this paper, we describe how we built a dataset from the raw videos and how we developed a pipeline to automate as much of the process …
Glioma is one of the most common types of brain tumors; it arises in the glial cells in the human brain and in the spinal cord. In addition to having a high mortality rate, glioma treatment is also very expensive. Hence, automatic and accurate segmentation and measurement from the early stages are critical in order to prolong the survival rates of the patients and to reduce the costs of the treatment. In the present work, we propose a novel end-to-end cascaded network for semantic segmentation that utilizes the hierarchical structure of the tumor sub-regions with ResNet-like blocks and Squeeze…
UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Siemens Healthineers（西门子医疗）；Annalise-AI, Pty, Ltd（Annalise人工智能私人有限公司）；Google Cloud AI（谷歌云人工智能）
Monash University Malaysia（莫纳什大学马来西亚分校）；Sunway Medical Centre（双威医疗中心）；The University of Oklahoma（俄克拉荷马大学）
Convolutional neural networks (CNNs) have been used quite successfully for semantic segmentation of brain tumors. However, current CNNs and attention mechanisms are stochastic in nature and neglect the morphological indicators used by radiologists to manually annotate regions of interest. In this paper, we introduce a channel and spatial wise asymmetric attention (CASPIAN) by leveraging the inherent structure of tumors to detect regions of saliency. To demonstrate the efficacy of our proposed layer, we integrate this into a well-established convolutional neural network (CNN) architecture to ac…
Automatic segmentation of brain tumors is an essential but challenging step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis, treatment planning and assessment. Multimodal Brain Tumor Segmentation Challenge 2020 (BraTS 2020) provides a common platform for comparing different automatic algorithms on multi-parametric Magnetic Resonance Imaging (mpMRI) in tasks of 1) Brain tumor segmentation MRI scans; 2) Prediction of patient overall survival (OS) from pre-operative MRI scans; 3) Distinction of true tumor recurrence from treatment related effects …
In this paper, we propose a novel technique for sampling sequential images using a cylindrical transform in a cylindrical coordinate system for kidney semantic segmentation in abdominal computed tomography (CT). The images generated from a cylindrical transform augment a limited annotated set of images in three dimensions. This approach enables us to train contemporary classification deep convolutional neural networks (DCNNs) instead of fully convolutional networks (FCNs) for semantic segmentation. Typical semantic segmentation models segment a sequential set of images (e.g. CT or video) by se…
Data Science Institute, Imperial College London（伦敦帝国理工学院数据科学研究所）；National Heart and Lung Institute, Imperial College London（伦敦帝国理工学院国家心肺研究所）
A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has emerged as a front-line diagnostic tool for brain tumors without ionizing radiation. Manual segmentation of brain tumor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on operator's experience. In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent. In this study, we p…
Three-dimensional (3D) kidney parsing on computed tomography angiography (CTA) images is of great clinical significance. Automatic segmentation of kidney, renal tumor, renal vein and renal artery benefits a lot on surgery-based renal cancer treatment. In this paper, we propose a new nnhra-unet network, and use a multi-stage framework which is based on it to segment the multi-structure of kidney and participate in the KiPA2022 challenge.
German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；University of Heidelberg（海德堡大学）
The U-Net is arguably the most successful segmentation architecture in the medical domain. Here we apply a 3D U-Net to the 2019 Kidney and Kidney Tumor Segmentation Challenge and attempt to improve upon it by augmenting it with residual and pre-activation residual blocks. Cross-validation results on the training cases suggest only very minor, barely measurable improvements. Due to marginally higher dice scores, the residual 3D U-Net is chosen for test set prediction. With a Composite Dice score of 91.23 on the test set, our method outperformed all 105 competing teams and won the KiTS2019 chall…
Kidney volume is greatly affected in several renal diseases. Precise and automatic segmentation of the kidney can help determine kidney size and evaluate renal function. Fully convolutional neural networks have been used to segment organs from large biomedical 3D images. While these networks demonstrate state-of-the-art segmentation performances, they do not immediately translate to small foreground objects, small sample sizes, and anisotropic resolution in MRI datasets. In this paper we propose a new framework to address some of the challenges for segmenting 3D MRI. These methods were impleme…
SimulaMet（SimulaMet研究院）；Oslo Metropolitan University（奥斯陆城市大学）；UiT The Arctic University of Norway（挪威北极大学）
Head and neck cancers are the fifth most common cancer worldwide, and recently, analysis of Positron Emission Tomography (PET) and Computed Tomography (CT) images has been proposed to identify patients with a prognosis. Even though the results look promising, more research is needed to further validate and improve the results. This paper presents the work done by team MLC for the 2022 version of the HECKTOR grand challenge held at MICCAI 2022. For Task 1, the automatic segmentation task, our approach was, in contrast to earlier solutions using 3D segmentation, to keep it as simple as possible …
MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset
Shanghai AI Lab（上海人工智能实验室）；University of Liverpool（利物浦大学）；Columbia university（哥伦比亚大学）；Monash University（墨尔本大学）；Xi'an Jiaotong-Liverpool University（西安交通大学利物浦大学）；MBZUAI（穆斯林人工智能研究所）；People's Hospital of Gong'an County（公安县人民医院）
本文提出首个 3D 全腹部病变多类分割数据集 MSWAL，覆盖七类常见病变，并提出 Inception nnU-Net 框架，在分割性能上显著优于现有方法。
Automatic segmentation of kidney and kidney tumour in Computed Tomography (CT) images is essential, as it uses less time as compared to the current gold standard of manual segmentation. However, many hospitals are still reliant on manual study and segmentation of CT images by medical practitioners because of its higher accuracy. Thus, this study focuses on the development of an approach for automatic kidney and kidney tumour segmentation in contrast-enhanced CT images. A method based on Convolutional Neural Network (CNN) was proposed, where a 3D U-Net segmentation model was developed and train…
Rowan University（罗文大学）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
Magnetic resonance imaging (MRI) is routinely used for brain tumor diagnosis, treatment planning, and post-treatment surveillance. Recently, various models based on deep neural networks have been proposed for the pixel-level segmentation of tumors in brain MRIs. However, the structural variations, spatial dissimilarities, and intensity inhomogeneity in MRIs make segmentation a challenging task. We propose a new end-to-end brain tumor segmentation architecture based on U-Net that integrates Inception modules and dilated convolutions into its contracting and expanding paths. This allows us to ex…
Johns Hopkins University（约翰霍普金斯大学）；University of Bologna（博洛尼亚大学）；Italian Institute of Technology（意大利理工学院）；University of California, San Francisco（加州大学旧金山分校）；Istanbul Medipol University（伊斯坦布尔Medipol大学）；University of Zurich（苏黎世大学）；ETH AI Center（ETH人工智能中心）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Xidian University（西安电子科技大学）；Weill Cornell Medical School, Cornell University（康奈尔大学威尔康奈尔医学院）
This paper proposes a novel cascaded U-Net for brain tumor segmentation. Inspired by the distinct hierarchical structure of brain tumor, we design a cascaded deep network framework, in which the whole tumor is segmented firstly and then the tumor internal substructures are further segmented. Considering that the increase of the network depth brought by cascade structures leads to a loss of accurate localization information in deeper layers, we construct many skip connections to link features at the same resolution and transmit detailed information from shallow layers to the deeper layers. Then…
Brain tumor segmentation from magnetic resonance imaging (MRI) plays an important role in diagnostic radiology. To overcome the practical issues in manual approaches, there is a huge demand for building automatic tumor segmentation algorithms. This work introduces an efficient brain tumor summation model by exploiting the advancement in MRI and graph neural networks (GNNs). The model represents the volumetric MRI as a region adjacency graph (RAG) and learns to identify the type of tumors through a graph attention network (GAT) -- a variant of GNNs. The ablation analysis conducted on two benchm…
Telecom Paris, Institut Polytechnique de Paris（巴黎综合理工学院电信学院）；Philips Research Paris（飞利浦巴黎研究院）；Imagine Institute, Universite de Paris（巴黎大学想象研究所）；Universite de Paris（巴黎大学）；Sorbonne Universite（索邦大学）
Due to a high heterogeneity in pose and size and to a limited number of available data, segmentation of pediatric images is challenging for deep learning methods. In this work, we propose a new CNN architecture that is pose and scale invariant thanks to the use of Spatial Transformer Network (STN). Our architecture is composed of three sequential modules that are estimated together during training: (i) a regression module to estimate a similarity matrix to normalize the input image to a reference one; (ii) a differentiable module to find the region of interest to segment; (iii) a segmentation …
A Study of Anatomical Priors for Deep Learning-Based Segmentation of Pheochromocytoma in Abdominal CT
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory（影像生物标记与计算机辅助诊断实验室）；Clinical Center, NIH（国家卫生研究院临床中心）；Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH（欧尼丝·凯蒂·施里弗国家儿童健康与人类发展研究所，国家卫生研究院）；National Cancer Institute, NIH（国家癌症研究所，国家卫生研究院）；National Institute of Diabetes and Digestive and Kidney Diseases, NIH（国家糖尿病、消化系统与肾病研究所，国家卫生研究院）
Uppsala University（乌普萨拉大学）；Antaros Medical AB（安塔罗斯医疗公司）
The UK Biobank is collecting extensive data on health-related characteristics of over half a million volunteers. The biological samples of blood and urine can provide valuable insight on kidney function, with important links to cardiovascular and metabolic health. Further information on kidney anatomy could be obtained by medical imaging. In contrast to the brain, heart, liver, and pancreas, no dedicated Magnetic Resonance Imaging (MRI) is planned for the kidneys. An image-based assessment is nonetheless feasible in the neck-to-knee body MRI intended for abdominal body composition analysis, wh…
University of Science and Technology Beijing（北京科技大学）；University of Central Florida（中佛罗里达大学）
Transformer, benefiting from global (long-range) information modeling using self-attention mechanism, has been successful in natural language processing and computer vision recently. Convolutional Neural Networks, capable of capturing local features, are difficult to model explicit long-distance dependencies from global feature space. However, both local and global features are crucial for dense prediction tasks, especially for 3D medical image segmentation. In this paper, we present the further attempt to exploit Transformer in 3D CNN for 3D medical image volumetric segmentation and propose a…
