Cargando…

A research on the improved rotational robustness for thoracic organ delineation by using joint learning of segmenting spatially‐correlated organs: A U‐net based comparison

PURPOSE: To study the improved rotational robustness by using joint learning of spatially‐correlated organ segmentation (SCOS) for thoracic organ delineation. The network structure is not our point. METHODS: The SCOS was implemented in a U‐net‐like model (abbr. SCOS‐net) and evaluated on unseen rota...

Descripción completa

Detalles Bibliográficos
Autores principales:	Zhang, Jie, Yang, Yiwei, Fang, Min, Xu, Yujin, Ji, Yongling, Chen, Ming
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	John Wiley and Sons Inc. 2023
Materias:	Radiation Oncology Physics
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10647980/ https://www.ncbi.nlm.nih.gov/pubmed/37469242 http://dx.doi.org/10.1002/acm2.14096

Ejemplares similares

Self‐configuring nnU‐Net for automatic delineation of the organs at risk and target in high‐dose rate cervical brachytherapy, a low/middle‐income country's experience
por: Duprez, Didier, et al.
Publicado: (2023)

DARU‐Net: A dual attention residual U‐Net for uterine fibroids segmentation on MRI
por: Zhang, Jian, et al.
Publicado: (2023)

Evaluation of deep learning‐based auto‐segmentation algorithms for delineating clinical target volume and organs at risk involving data for 125 cervical cancer patients
por: Wang, Zhi, et al.
Publicado: (2020)

Clinical target volume and organs at risk segmentation for rectal cancer radiotherapy using the Flex U-Net network
por: Sha, Xue, et al.
Publicado: (2023)

Deep learning‐based classification of organs at risk and delineation guideline in pelvic cancer radiation therapy
por: Lempart, Michael, et al.
Publicado: (2023)

Deep convolutional neural networks for automatic segmentation of thoracic organs‐at‐risk in radiation oncology – use of non‐domain transfer learning
por: Vu, Charles C., et al.
Publicado: (2020)

Deep learning‐based classification and structure name standardization for organ at risk and target delineations in prostate cancer radiotherapy
por: Jamtheim Gustafsson, Christian, et al.
Publicado: (2021)

Comparison of Eclipse Smart Segmentation and MIM Atlas Segment for liver delineation for yttrium‐90 selective internal radiation therapy
por: Li, Jun, et al.
Publicado: (2022)

RefineNet‐based 2D and 3D automatic segmentations for clinical target volume and organs at risks for patients with cervical cancer in postoperative radiotherapy
por: Xiao, Chengjian, et al.
Publicado: (2022)

A New Tumor Delineation Method for Brain Metastases Radiotherapy by Jointly Referring to Contrast-Enhanced T1-Weighted and Fluid-Attenuated Inversion Recovery MRI
por: Soyama, Maki, et al.
Publicado: (2020)

Delineation of Oral Mucosa as a Pseudo-Organ-at-Risk May Lead to a Decrease in the Incidence of Oral Mucositis: A Dosimetric Analysis of Intensity-Modulated Radiation Therapy Plans in Head and Neck Cancers
por: Khattar, Himanshi, et al.
Publicado: (2022)

Delineation of Radiation Therapy Target Volumes for Lingual Nerve Involvement
por: Sozio, Stephen J, et al.
Publicado: (2022)

Is Halcyon feasible for single thoracic or lumbar vertebral segment SBRT?
por: Li, Fang, et al.
Publicado: (2021)

Optimal virtual monoenergetic image in “TwinBeam” dual‐energy CT for organs‐at‐risk delineation based on contrast‐noise‐ratio in head‐and‐neck radiotherapy
por: Wang, Tonghe, et al.
Publicado: (2019)

Structure boundary-preserving U-Net for prostate ultrasound image segmentation
por: Bi, Hui, et al.
Publicado: (2022)

Local Recurrence Following Total Marrow Radiation: Implications for Clinical Target Delineation
por: Hara, Jared H, et al.
Publicado: (2020)

Improved 3D U-Net robustness against JPEG 2000 compression for male pelvic organ segmentation in radiotherapy
por: El Khoury, Karim, et al.
Publicado: (2021)

PCG-net: feature adaptive deep learning for automated head and neck organs-at-risk segmentation
por: Luan, Shunyao, et al.
Publicado: (2023)

Multiatlas segmentation of thoracic and abdominal anatomy with level set‐based local search
por: Schreibmann, Eduard, et al.
Publicado: (2014)

Kidney Tumor Segmentation Based on FR2PAttU-Net Model
por: Sun, Peng, et al.
Publicado: (2022)

Oral Gastrografin Facilitates Delineation of Intestinal Tracts in CT-Based Brachytherapy for Uterine Cervical Cancer
por: Irie, Daisuke, et al.
Publicado: (2020)

Achieving Exceptional Cochlea Delineation in Radiotherapy Scans: The Impact of Optimal Window Width and Level Settings
por: Shanbhag, Nandan M, et al.
Publicado: (2023)

SCAU-Net: Spatial-Channel Attention U-Net for Gland Segmentation
por: Zhao, Peng, et al.
Publicado: (2020)

Automatic renal mass segmentation and classification on CT images based on 3D U-Net and ResNet algorithms
por: Zhao, Tongtong, et al.
Publicado: (2023)

Markerless Liver Tumor Localization Using Internal Liver Volume Delineated By Four-Dimensional Cone-Beam CT
por: Mizukami, Naohisa, et al.
Publicado: (2021)

AttR2U-Net: A Fully Automated Model for MRI Nasopharyngeal Carcinoma Segmentation Based on Spatial Attention and Residual Recurrent Convolution
por: Zhang, Jiajing, et al.
Publicado: (2022)

Physician review of image registration and normal structure delineation
por: Turchan, William Tyler, et al.
Publicado: (2020)

Dose and organ displacement comparisons with breast conservative radiotherapy using abdominal and thoracic deep‐inspiration breath‐holds: A comparative dosimetric study
por: Matsumoto, Yoshitsugu, et al.
Publicado: (2023)

Multi-Task Deep Supervision on Attention R2U-Net for Brain Tumor Segmentation
por: Ma, Shiqiang, et al.
Publicado: (2021)

Geometric and Dosimetric Evaluation of the Automatic Delineation of Organs at Risk (OARs) in Non-Small-Cell Lung Cancer Radiotherapy Based on a Modified DenseNet Deep Learning Network
por: Zhang, Fuli, et al.
Publicado: (2022)

DCTR U-Net: automatic segmentation algorithm for medical images of nasopharyngeal cancer in the context of deep learning
por: Zeng, Yan, et al.
Publicado: (2023)

The Efficacy of Hemostatic Radiotherapy for Advanced Malignancies Assessed by World Health Organization Bleeding Status
por: Katano, Atsuto, et al.
Publicado: (2021)

Robustness and reproducibility of an artificial intelligence‐assisted online segmentation and adaptive planning process for online adaptive radiation therapy
por: Chapman, John W., et al.
Publicado: (2022)

The Effect of Thoracic Radiotherapy on the Quality of Life in Lung Cancer Patients
por: Ay Eren, Ayfer, et al.
Publicado: (2021)

Evaluation of tomotherapy MVCT image enhancement program for tumor volume delineation
por: Martin, Spencer, et al.
Publicado: (2011)

Interactive contour delineation and refinement in treatment planning of image‐guided radiation therapy
por: Zhou, Wu, et al.
Publicado: (2014)

Multiple U-Net-Based Automatic Segmentations and Radiomics Feature Stability on Ultrasound Images for Patients With Ovarian Cancer
por: Jin, Juebin, et al.
Publicado: (2021)

An open-source nnU-net algorithm for automatic segmentation of MRI scans in the male pelvis for adaptive radiotherapy
por: Lorenzen, Ebbe Laugaard, et al.
Publicado: (2023)

Inter-Observer Variation in Delineating the Pharyngeal Constrictor Muscle as Organ at Risk in Radiotherapy for Head and Neck Cancer
por: Petkar, Imran, et al.
Publicado: (2021)

Multiscale U-Net with Spatial Positional Attention for Retinal Vessel Segmentation
por: Liu, Congjun, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_v626v6ksv4ndm7b9d2uqb4eqou