Page 27 - 《中国医疗器械杂志》2026年第1期

Chinese Journal of Medical Instrumentation 2026年 第50卷 第1期

医 学 人 工 智 能

connectivity of the posterior hippocampus is more planning: a review[J]. J Med Imaging Radiat Oncol, 2021,
dominant as we age[J]. Cogn Neurosci, 2014, 5(3-4): 65(5): 578-595.
150-159. [19] SHI F, HU W, WU J, et al. Deep learning empowered
[9] 李俊玉, 李莹, 谭文勇. 放疗期间危及器官中的亚结构 volume delineation of whole-body organs-at-risk for
保护新策略[J]. 肿瘤防治研究, 2014, 41(5): 482-487. accelerated radiotherapy[J]. Nat Commun, 2022, 13(1):
[10] SHRESTHA S, GUPTA A C, BATES J E, et al. 6566.
Development and validation of an age-scalable cardiac [20] MINAEE S, BOYKOV Y, PORIKLI F, et al. Image
model with substructures for dosimetry in late-effects segmentation using deep learning: a survey[J]. IEEE
studies of childhood cancer survivors[J]. Radiother Trans Pattern Anal Mach Intell, 2022, 44(7): 3523-3542.
Oncol, 2020, 153: 163-171. [21] HE X Q, XU W J, YANG J, et al. Deep convolutional
[11] MERCHANT T E, KIEHNA E N, LI C H, et al. neural network with a multi-scale attention feature fusion
Modeling radiation dosimetry to predict cognitive module for segmentation of multimodal brain tumor[J].
outcomes in pediatric patients with CNS embryonal
Front Neurosci, 2021, 26(15): 782968.
tumors including medulloblastoma[J]. Int J Radiat Oncol
[22] RINIVASAN S, DURAIRAJU K, DEEBA K, et al.
Biol Phys, 2006, 65(1): 210-221.
Multimodal biomedical image segmentation using multi-
[12] MEKKI L, ACHARYA S, LADRA M, et al. Deep
dimensional U-convolutional neural network[J]. BMC
learning segmentation of organs-at-risk with integration
Med Imaging, 2024, 24(1): 38.
into clinical workflow for pediatric brain radiotherapy[J].
[23] 张富利, 崔德琪, 王秋生, 等. 基于深度学习和图谱库方
J Appl Clin Med Phys, 2024, 25(3): e14310.
法自动勾画肿瘤放疗中危及器官的比较[J]. 中国医学
[13] AJITHKUMAR T, HORAN G, PADOVANI L, et al.
物理学杂志, 2019, 36(12): 1486-1490.
SIOPE-Brain tumor group consensus guideline on
[24] 戴相昆, 王小深, 杜乐辉, 等. 基于三维U-NET深度卷积
craniospinal target volume delineation for high-precision
神经网络的头颈部危及器官的自动勾画[J]. 生物医学
radiotherapy[J]. Radiother Oncol, 2018, 128(2): 192-197.
工程学杂志, 2020, 37(1): 136-141.
[14] HOEBEN B A, CARRIE C, TIMMERMANN B, et al.
[25] PORTER E, FUENTES P, SIDDIQUI Z, et al.
Management of vertebral radiotherapy dose in paediatric
Hippocampus segmentation on noncontrast CT using
patients with cancer: consensus recommendations from
deep learning[J]. Med Phys, 2020, 47(7): 2950-2961.
the SIOPE radiotherapy working group[J]. Lancet Oncol,
[26] 张瑞萍, 刘应龙, 张文静, 等. 基于人工智能的多模态影
2019, 20(3): e155-e166.
像辅助海马体自动勾画研究[J]. 中国医学物理学杂志,
[15] EEKERS D B, VEN L I, ROELOFS E, et al. The EPTN
2022, 39(3): 390-396.
consensus-based atlas for CT-and MR-based contouring
[27] QIU W, ZHANG W, MA X, et al. Auto-segmentation of
in neuro-oncology[J]. Radiother Oncol, 2018, 128(1): 37-
important centers of growth in the pediatric skeleton to
43.
[16] GONDI V, PUGH S L, TOME W A, et al. Preservation consider during radiation therapy based on deep
of memory with conformal avoidance of the hippocampal learning[J]. Med Phys, 2023, 50(1): 284-296.
neural stem-cell compartment during whole-brain [28] 肖江喜, 郭雪梅, 谢晟, 等. 应用扩散张量成像对正常儿
radiotherapy for brain metastases(RTOG 0933): a phase 童脑白质发育的初步研究[J]. 中华放射学杂志, 2005,
Ⅱ multi-institutional trial[J]. J Clin Oncol, 2014, 32(34): 39(12): 1252-1255.
3810-3816. [29] PAUS T, ZIJDENBOS A, WORSLEY K, et al. Structural
[17] LUSTBERG T, VAN SOEST J, GOODING M, et al. maturation of neural pathways in children and
Clinical evaluation of atlas and deep learning based adolescents: in vivo study[J]. Science, 1999, 283(5409):
automatic contouring for lung cancer[J]. Radiother Oncol, 1908-1911.
2018, 126(2): 312-317. [30] CANAVESE F, DIMEGLIO A . Normal and abnormal
[18] SAMARASINGHE G, JAMESON M, VINOD S, et al. spine and thoracic cage development[J]. World J Orthop,
Deep learning for segmentation in radiation therapy 2013, 4(4): 167-174.

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