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Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network

Zhikai Liu, Xia Liu, Bin Xiao, Shaobin Wang, Zheng Miao, Yuliang Sun, Fuquan Zhang
2020 Physica medica (Testo stampato)  
We introduced and evaluated an end-to-end organs-at-risk (OARs) segmentation model that can provide accurate and consistent OARs segmentation results in much less time.  ...  We collected 105 patients' Computed Tomography (CT) scans that diagnosed locally advanced cervical cancer and treated with radiotherapy in one hospital.  ...  Acknowledgement The work that was described in this paper was supported by a grant from the Ministry of Science and Technology of the People's Republic of China (Grant Number 2016YFC0105207).  ... 
doi:10.1016/j.ejmp.2019.12.008 pmid:31918371 fatcat:mkhdzedzejh7zmpbvn2nedmyoq

Deep Learning in Multi-organ Segmentation [article]

Yang Lei, Yabo Fu, Tonghe Wang, Richard L.J. Qiu, Walter J. Curran, Tian Liu, Xiaofeng Yang
2020 arXiv   pre-print
datasets and 2015 MICCAI Head Neck Auto-Segmentation Challenge datasets.  ...  We provided a comprehensive comparison among DL-based methods for thoracic and head & neck multiorgan segmentation using benchmark datasets, including the 2017 AAPM Thoracic Auto-segmentation Challenge  ...  ACKNOWLEDGEMENT This research was supported in part by the National Cancer Institute of the National Institutes of Health under Award Number R01CA215718 and Emory Winship Cancer Institute pilot grant.  ... 
arXiv:2001.10619v1 fatcat:6uwqwnzydzccblh5cajhsgdpea

AttentionAnatomy: A unified framework for whole-body organs at risk segmentation using multiple partially annotated datasets [article]

Shanlin Sun, Yang Liu, Narisu Bai, Hao Tang, Xuming Chen, Qian Huang, Yong Liu, Xiaohui Xie
2020 arXiv   pre-print
Recently, deep learning based methods for OAR delineation have been proposed and applied in clinical practice for separate regions of the human body (head and neck, thorax, and abdomen).  ...  In this paper, our proposed end-to-end convolutional neural network model, called AttentionAnatomy, can be jointly trained with three partially annotated datasets, segmenting OARs from whole body.  ...  Recently, Deep Convolutional Neural Networks(DCNNs) methods have been successfully applied to different medical image segmentation tasks [1, 2] , including OAR delineation [3, 4] .  ... 
arXiv:2001.04446v1 fatcat:o3lwbdbc7fg2zncmtfpngtt6oa

Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm

Minh-Trieu Tran, Soo-Hyung Kim, Hyung-Jeong Yang, Guee-Sang Lee, In-Jae Oh, Sae-Ryung Kang
2021 Sensors  
One essential step in radiotherapy treatment planning is the organ at risk of segmentation in Computed Tomography (CT).  ...  However, among the above organs, the esophagus is one of the most difficult organs to segment because of its small size, ambiguous boundary, and very low contrast in CT images.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21134556 fatcat:vjtzoctckrfhthhydzqnh6bqfa

Deep Learning for Automated Medical Image Analysis [article]

Wentao Zhu
2019 arXiv   pre-print
diagnosed malignant cancer, and 3) head and neck CT images for automated delineation of organs at risk in radiotherapy.  ...  Medical imaging is an essential tool in many areas of medical applications, used for both diagnosis and treatment.  ...  lung cancers, the most frequently diagnosed malignant cancer, and 3) head and neck CT images for automated delineation of organs at risk in radiotherapy.  ... 
arXiv:1903.04711v1 fatcat:xigyugddlrentc42o5mnlbhdkq

Automatic Segmentation of Mandible from Conventional Methods to Deep Learning—A Review

Bingjiang Qiu, Hylke van der Wel, Joep Kraeima, Haye Hendrik Glas, Jiapan Guo, Ronald J. H. Borra, Max Johannes Hendrikus Witjes, Peter M. A. van Ooijen
2021 Journal of Personalized Medicine  
Accurate segmentation of the mandible from head and neck (H&N) scans is an important step in order to build a personalized 3D digital mandible model for 3D printing and treatment planning of OMFS.  ...  With the advancement of computer vision approaches, researchers have developed several algorithms to automatically segment the mandible during the last two decades.  ...  Acknowledgments: The author is supported by a joint Ph.D. fellowship from China Scholarship Council. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/jpm11070629 doaj:8c2ebb1623014e3994cfdc37bcaa5019 fatcat:t3d2qltuajfrjmyhqod57biogi

Testing Segmentation Popular Loss and Variations in Three Multiclass Medical Imaging Problems

Pedro Furtado
2021 Journal of Imaging  
Image structures are segmented automatically using deep learning (DL) for analysis and processing.  ...  Taking three different medical image segmentation problems (segmentation of organs in magnetic resonance images (MRI), liver in computer tomography images (CT) and diabetic retinopathy lesions in eye fundus  ...  Acknowledgments: We used publicly available MRI, CT and IDRID datasets for our experiments. The references for the datasets are [10, 37] .  ... 
doi:10.3390/jimaging7020016 pmid:34460615 fatcat:nfizwg2kunhqdc5etxcqzs7biu

Application of Deep Convolution Network to Automated Image Segmentation of Chest CT for Patients With Tumor

Hui Xie, Jian-Fang Zhang, Qing Li
2021 Frontiers in Oncology  
The images in the validation set were used to validate the 8 networks in terms of the automated identification and delineation of organs, in order to obtain the optimal segmentation model of each network  ...  ObjectivesTo automate image delineation of tissues and organs in oncological radiotherapy by combining the deep learning methods of fully convolutional network (FCN) and atrous convolution (AC).MethodsA  ...  ACKNOWLEDGMENTS Thanks for the support from the above funding and Shenzhen Yino Intelligence Techonology Co, Ltd.  ... 
doi:10.3389/fonc.2021.719398 pmid:34660284 pmcid:PMC8511825 fatcat:bpkvpem4bvccvl3iatmjl7phsm

A convolutional neural network-based system to classify patients using FDG PET/CT examinations

Keisuke Kawauchi, Sho Furuya, Kenji Hirata, Chietsugu Katoh, Osamu Manabe, Kentaro Kobayashi, Shiro Watanabe, Tohru Shiga
2020 BMC Cancer  
In addition, we performed a region-based analysis of CNN (head-and-neck, chest, abdomen, and pelvic region).  ...  In region-based analysis, the prediction was correct with the probability of 97.3% (head-and-neck), 96.6% (chest), 92.8% (abdomen) and 99.6% (pelvic region), respectively.  ...  , and 3) equivocal in the head-and-neck region.  ... 
doi:10.1186/s12885-020-6694-x pmid:32183748 fatcat:hjp7iplwabhlhat56waqg5bdwq

Automatic hyoid bone detection in fluoroscopic images using deep learning

Zhenwei Zhang, James L. Coyle, Ervin Sejdić
2018 Scientific Reports  
To automatically detect the location of the hyoid bone in a frame, we proposed a single shot multibox detector, a deep convolutional neural network, which is employed to detect and classify the location  ...  Dysphagia is a frequent clinical sign in patients with stroke, head and neck cancer and a variety of other medical conditions 2-4 .  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.  ... 
doi:10.1038/s41598-018-30182-6 pmid:30120314 pmcid:PMC6097989 fatcat:ri4sk23ogjelxeavz2lj55jgqm

A survey on deep learning in medical image analysis

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez
2017 Medical Image Analysis  
We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area.  ...  Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images.  ...  Papers not reporting results on medical image data or only using standard feed-forward neural networks with handcrafted features were excluded.  ... 
doi:10.1016/ pmid:28778026 fatcat:esbj72ftwvbgzh6jgw367k73j4

DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT [article]

Mingyuan Meng, Bingxin Gu, Lei Bi, Shaoli Song, David Dagan Feng, Jinman Kim
2022 arXiv   pre-print
, and locations) derived from the segmentation backbone.  ...  Our novelty is the introduction of a hard-sharing segmentation backbone to guide the extraction of local features related to the primary tumors, which reduces the interference from non-relevant background  ...  ., head and neck cancer [42] ) or other imaging modalities (e.g., MRI and CT). VII.  ... 
arXiv:2109.07711v2 fatcat:7g6rz2hhwzcqdkdzvjdyvd3qzy

Medical Imaging Biomarker Discovery and Integration Towards AI-Based Personalized Radiotherapy

Yaru Pang, Hui Wang, He Li
2022 Frontiers in Oncology  
With traditional radiomics and artificial intelligence (AI) techniques, we can identify biological target volume from functional images against conventional GTV derived from anatomical imaging.  ...  GTV, CTV and PTV can be prescribed at different dose levels, however, there is an emphasis that their dose distributions need to be uniform, despite the fact that most types of tumour are heterogeneous  ...  ACKNOWLEDGMENTS The authors are grateful for the support of the China Scholarship Council.  ... 
doi:10.3389/fonc.2021.764665 pmid:35111666 pmcid:PMC8801459 fatcat:btb5nr36zferzc5hcz72d7yxbe

Mandible Segmentation of Dental CBCT Scans Affected by Metal Artifacts Using Coarse-to-Fine Learning Model

Bingjiang Qiu, Hylke van der Wel, Joep Kraeima, Haye Hendrik Glas, Jiapan Guo, Ronald J. H. Borra, Max Johannes Hendrikus Witjes, Peter M. A. van Ooijen
2021 Journal of Personalized Medicine  
In this work, we propose a novel coarse-to-fine segmentation framework based on 3D convolutional neural network and recurrent SegUnet for mandible segmentation in CBCT scans.  ...  The method was evaluated using a dental CBCT dataset. In addition, we evaluated the proposed method and compared it with state-of-the-art methods in two CT datasets.  ...  The authors would like to acknowledge the support received from NVIDIA by providing a GPU as part of their GPU grant program.  ... 
doi:10.3390/jpm11060560 fatcat:mg74ubnkxvewxf2luuzm6w36eq

From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities [article]

Parnian Afshar, Arash Mohammadi, Konstantinos N. Plataniotis, Anastasia Oikonomou, Habib Benali
2019 arXiv   pre-print
The conventional Radiomics workflow is typically based on extracting pre-designed features (also referred to as hand-crafted or engineered features) from a segmented region of interest.  ...  Radiomics is an emerging and relatively new research field, which refers to extracting semi-quantitative and/or quantitative features from medical images with the goal of developing predictive and/or prognostic  ...  (iii) Adaptive Voting, where a weight of importance for each model (HCR and DLR) is learned for example using a separate neural network.  ... 
arXiv:1808.07954v3 fatcat:huc23wcklfey5aetnlbe6o4h34
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