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Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Integrate Framework: Application to Liver Tumor and Vessel 3D Reconstruction under embedded microprocessor [article]

Xiangyu Meng, Xudong Zhang, Gan Wang, Ying Zhang, Xin Shi, Huanhuan Dai, Zixuan Wang, Xun Wang
2022 arXiv   pre-print
Segmentation and labeling of liver tumors and blood vessels in CT images can provide convenience for doctors in liver tumor diagnosis and surgical intervention.  ...  This network achieved very competitive performance for liver vessel and liver tumor segmentation tasks, meanwhile it can improve the recognition of morphologic margins of liver tumors by exploiting the  ...  Due to the different characteristics of CT images in the two phases, we need to train two models for automatic segmentation of arterial vessels and tumors.  ... 
arXiv:2111.13299v4 fatcat:v5wymgybkfccrltkjfweezegva

Improving CT Image Tumor Segmentation Through Deep Supervision and Attentional Gates

Alžběta Turečková, Tomáš Tureček, Zuzana Komínková Oplatková, Antonio Rodríguez-Sánchez
2020 Frontiers in Robotics and AI  
In this paper, we propose the extension of a popular deep learning methodology, Convolutional Neural Networks (CNN), by including deep supervision and attention gates.  ...  The segmentation of areas in the CT images provides a valuable aid to physicians and radiologists in order to better provide a patient diagnose.  ...  ACKNOWLEDGMENTS This paper was created with support of A.I. Lab ( from Tomas Bata University in Zlin and IIS group at the University of Innsbruck (  ... 
doi:10.3389/frobt.2020.00106 pmid:33501273 pmcid:PMC7805665 fatcat:iaur44a4dnd4zbcc5my6ffupza

State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma

Anna Castaldo, Davide Raffaele De Lucia, Giuseppe Pontillo, Marco Gatti, Sirio Cocozza, Lorenzo Ugga, Renato Cuocolo
2021 Diagnostics  
While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy.  ...  In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications.  ...  This solution automatically detects the contours of liver in PET images. In Table 1 , a summary of the reviewed papers is presented.  ... 
doi:10.3390/diagnostics11071194 fatcat:zmwt7urwinac7bshxcxm6q7doe

PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images [article]

Yingying Xu, Ming Cai, Lanfen Lin, Yue Zhang, Hongjie Hu, Zhiyi Peng, Qiaowei Zhang, Qingqing Chen, Xiongwei Mao, Yutaro Iwamoto, Xian-Hua Han, Yen-Wei Chen (+1 others)
2021 arXiv   pre-print
In this paper, we propose a phase attention residual network (PA-ResSeg) to model multi-phase features for accurate liver tumor segmentation, in which a phase attention (PA) is newly proposed to additionally  ...  exploit the images of arterial (ART) phase to facilitate the segmentation of portal venous (PV) phase.  ...  The public datasets of liver tumor only provide single-phase images of PV phase. The study of liver tumor segmentation based on multi-phase CT images is restricted by dataset acquisition.  ... 
arXiv:2103.00274v1 fatcat:5p4asf4l7vafpjhxj4ywzqu2ku

Modality specific U-Net variants for biomedical image segmentation: A survey [article]

Narinder Singh Punn, Sonali Agarwal
2022 arXiv   pre-print
image segmentation to address the automation in identification and detection of the target regions or sub-regions.  ...  Finally, the strengths and similarities of these U-Net variants are analysed along with the challenges involved in biomedical image segmentation to uncover promising future research directions in this  ...  Acknowledgment We thank our institute, Indian Institute of Information Technology Allahabad (IIITA), India and Big Data Analytics (BDA) lab for allocating the necessary  ... 
arXiv:2107.04537v4 fatcat:m5oqea5q6vhbhkerjmejder3hu

Automatic CT Segmentation from Bounding Box Annotations using Convolutional Neural Networks [article]

Yuanpeng Liu, Qinglei Hui, Zhiyi Peng, Shaolin Gong, Dexing Kong
2021 arXiv   pre-print
To address this problem, we proposed an automatic CT segmentation method based on weakly supervised learning, by which one could train an accurate segmentation model only with weak annotations in the form  ...  For liver, spleen and kidney segmentation, it achieved an accuracy of 95.19%, 92.11%, and 91.45%, respectively.  ...  Acknowledgment The authors thank the organizers of the LiTS challenge, KiTS19 challenge, and MSD segmentation challenge for providing CT segmentation data.  ... 
arXiv:2105.14314v3 fatcat:emqsunxbxne2fpptiahyv3mxmi

Application of deep learning algorithm in feature mining and rapid identification of colorectal image

Mingchao Du, Min Tao, Jian Hong, Dian Zhou, Shuihua Wang
2020 IEEE Access  
Drawing on the ideas of multi-factor Meta-regression analysis widely used in the medical field and the model aggregation framework based on Bayesian prior probability theory, a prognostic model of colorectal  ...  Based on deep learning technology, this paper proposes a two-stage colorectal image feature mining and fast recognition model to achieve fully automatic medical image pathology discrimination.  ...  physical examination, enteroscopy, detection of serum tumor markers (CEA, CA19-9), CT, MRI, positron emission tomography / CT imaging if necessary.  ... 
doi:10.1109/access.2020.3008000 fatcat:jwvvr4pxijajzprs32ueeaaw7e

Liver Cancer Detection using Hybridized Fully Convolutional Neural Network based on Deep learning framework

Xin Dong, Yizhao Zhou, Lantian Wang, Jingfeng Peng, Yanbo Lou, Yiqun Fan
2020 IEEE Access  
The segmentation of liver lesions in CT images can be used to assess the tumor load, plan treatments predict, and monitor the clinical response.  ...  However, a deep end-to-end learning approach to help discrimination in abdominal CT images of the liver between liver metastases of colorectal cancer and benign cysts has been analyzed.  ...  BACKGROUND SURVEY AND IMPORTANCE FEATURE OF THIS RESEARCH Bai et al. [25] proposed the Multi-scale candidate generation (MCG) for the liver tumor segmentation approach on CT images.  ... 
doi:10.1109/access.2020.3006362 fatcat:uyoeywroozefzf5jtxldytpfoi

Combining deep learning with anatomy analysis for segmentation of portal vein for liver SBRT planning

Bulat Ibragimov, Diego Toesca, Daniel Chang, Albert Koong, Lei Xing
2017 Physics in Medicine and Biology  
We apply convolutional neural networks (CNN) to learn consistent appearance patterns of PV using a training set of CT images with reference annotations and then enhance PV in previously unseen CT images  ...  In this paper, we propose a novel framework for automated PV segmentation from computed tomography (CT) images.  ...  Acknowledgments This work was partially supported by NIH (1R01 CA176553 and EB016777) , and Google and Varian research grants.  ... 
doi:10.1088/1361-6560/aa9262 pmid:28994665 pmcid:PMC5739057 fatcat:57mfdxpvmfdshhykm7bayviqce

Deep Learning of Unified Region, Edge, and Contour Models for Automated Image Segmentation [article]

Ali Hatamizadeh
2020 arXiv   pre-print
Recently, convolutional neural networks (CNNs) have gained traction in the design of automated segmentation pipelines.  ...  In this thesis, we devise novel methodologies that address these issues and establish robust representation learning frameworks for fully-automatic semantic segmentation in medical imaging and mainstream  ...  [9] segmented the lung lobes in the expiration phase based on a prior lobe segmentation mask obtained from a CT image acquired in the inspiration phase.  ... 
arXiv:2006.12706v1 fatcat:6jchhrv6zrhlhbpcak6fcbh4a4

Deep Learning in Selected Cancers' Image Analysis—A Survey

Taye Girma Debelee, Samuel Rahimeto Kebede, Friedhelm Schwenker, Zemene Matewos Shewarega
2020 Journal of Imaging  
The result of the review process indicated that deep learning methods have achieved state-of-the-art in tumor detection, segmentation, feature extraction and classification.  ...  Moreover, the application of deep learning to imaging devices for the detection of various cancer cases has been studied by researchers affiliated to academic and medical institutes in economically developed  ...  [72] explored the application of two versions of regions with convolutional neural networks (R-CNN), Mask R-CNN and Mask X R-CNN, on three different dataset for cervix classification for automatic segmentation  ... 
doi:10.3390/jimaging6110121 pmid:34460565 fatcat:2xvx5uya25a23nxicq3hdl42hi

The Liver Tumor Segmentation Benchmark (LiTS) [article]

Patrick Bilic, Patrick Ferdinand Christ, Eugene Vorontsov, Grzegorz Chlebus, Hao Chen, Qi Dou, Chi-Wing Fu, Xiao Han, Pheng-Ann Heng, Jürgen Hesser, Samuel Kadoury, Tomasz Konopczyǹski (+44 others)
2019 arXiv   pre-print
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International  ...  Twenty four valid state-of-the-art liver and liver tumor segmentation algorithms were applied to a set of 131 computed tomography (CT) volumes with different types of tumor contrast levels (hyper-/hypo-intense  ...  CNNs (U-Net) for liver and tumor segmentation [2]  ... 
arXiv:1901.04056v1 fatcat:25ekt2znl5adnd5laap4ez6a4y

CHAOS Challenge – Combined (CT-MR) Healthy Abdominal Organ Segmentation [article]

A. Emre Kavur, N. Sinem Gezer, Mustafa Barış, Sinem Aslan, Pierre-Henri Conze, Vladimir Groza, Duc Duy Pham, Soumick Chatterjee, Philipp Ernst, SavaşÖzkan, Bora Baydar, Dmitry Lachinov (+15 others)
2020 arXiv   pre-print
Imaging (ISBI), 2019, in Venice, Italy.  ...  CHAOS provides both abdominal CT and MR data from healthy subjects for single and multiple abdominal organ segmentation.  ...  Liver (CT), liver tumor (CT), spleen (CT), hepatic vessels in the liver (CT), pancreas and pancreas tumor (CT) MICCAI 2018, Spain KiTS19 Single model segmentation Kidney and kidney tumor (CT  ... 
arXiv:2001.06535v2 fatcat:djarkr365felddvi43vq46bnsq

Front Matter: Volume 11317

Barjor S. Gimi, Andrzej Krol
2020 Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging  
in Molecular, Structural, and Functional Imaging, Innovations in Image Processing, Neurological Imaging, Novel Imaging Techniques and Applications, Ocular and Optical Imaging, Vascular and Pulmonary Imaging  ...  The diverse sessions included Keynote and Invited Talk, Bone and Skeletal Imaging, Segmentation, Registration and Decision-making, Cardiac Imaging and Nanoparticle Imaging, Deep Convolutional Neural Networks  ...  to a machine learning aneurysm identifier 11317 20 Brain tumor segmentation using 3D mask R-CNN for dynamic susceptibility contrast enhanced perfusion imaging 11317 21 11317 22 Integration of multi-task  ... 
doi:10.1117/12.2570187 fatcat:5hec55iwfneuhbhtjbqjcf2jqu

Artificial Intelligence in Quantitative Ultrasound Imaging: A Review [article]

Boran Zhou, Xiaofeng Yang, Tian Liu
2020 arXiv   pre-print
Therefore, it is in great need to develop automatic method to improve the imaging quality and aid in measurements in QUS.  ...  Despite its safety and efficacy, QUS suffers from several major drawbacks: poor imaging quality, inter- and intra-observer variability which hampers the reproducibility of measurements.  ...  Hu et al. proposed to combine a dilated fully convolutional network (DFCN) with a phase-based active contour model for automatic tumor segmentation in BUS images [140] .  ... 
arXiv:2003.11658v1 fatcat:iujuh7gra5ax7od2gxoo6yrbpe
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