基于深度学习的牡丹内动脉自动分割技术在局部前列腺癌的确定性放疗治疗规划中的应用

Anjali Balagopal, Michael Dohopolski, Young Suk Kwon, Steven Montalvo, Howard Morgan, Ti Bai, Dan Nguyen, Xiao Liang, Xinran Zhong, Mu-Han Lin, Neil Desai, Steve Jiang
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引用次数: 0

摘要

背景和目的放疗诱发的勃起功能障碍(RiED)通常会影响前列腺癌患者,这促使各机构开展临床试验,探索通过对内腓动脉(IPA)进行剂量节省来保持性能力。IPA的分段具有挑战性,传统上不被视为高危器官(OAR)。本研究提出了一种针对IPA的深度学习(DL)自动分割模型,可使用计算机断层扫描(CT)和磁共振成像(MRI)或仅使用CT,以适应不同的临床实践。我们将数据分为 42/14/30,分别用于模型训练、测试和临床观察研究。该模型有三大创新:1)我们设计了一个具有挤压-激发块和模态关注的架构,以实现有效的特征提取和准确的分割;2)使用了一个新的损失函数,以在有噪声标签的情况下有效地训练模型;3)使用了模态剔除策略,使模型能够在没有核磁共振成像的情况下进行分割。人工智能分割的轮廓显示出与专家医师轮廓的剂量学相似性。观察者研究表明,人工智能轮廓(平均 = 3.7)的得分高于无经验医生的轮廓(平均 = 3.1)。结论所提出的模型可获得高质量的 IPA 轮廓,从而提高分割的统一性,并促进将标准化 IPA 分割引入临床试验和实践中。
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Deep learning based automatic segmentation of the Internal Pudendal Artery in definitive radiotherapy treatment planning of localized prostate cancer

Background and purpose

Radiation-induced erectile dysfunction (RiED) commonly affects prostate cancer patients, prompting clinical trials across institutions to explore dose-sparing to internal-pudendal-arteries (IPA) for preserving sexual potency. IPA, challenging to segment, isn't conventionally considered an organ-at-risk (OAR). This study proposes a deep learning (DL) auto-segmentation model for IPA, using Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) or CT alone to accommodate varied clinical practices.

Materials and methods

A total of 86 patients with CT and MRI images and noisy IPA labels were recruited in this study. We split the data into 42/14/30 for model training, testing, and a clinical observer study, respectively. There were three major innovations in this model: 1) we designed an architecture with squeeze-and-excite blocks and modality attention for effective feature extraction and production of accurate segmentation, 2) a novel loss function was used for training the model effectively with noisy labels, and 3) modality dropout strategy was used for making the model capable of segmentation in the absence of MRI.

Results

Test dataset metrics were DSC 61.71 ± 7.7 %, ASD 2.5 ± .87 mm, and HD95 7.0 ± 2.3 mm. AI segmented contours showed dosimetric similarity to expert physician’s contours. Observer study indicated higher scores for AI contours (mean = 3.7) compared to inexperienced physicians’ contours (mean = 3.1). Inexperienced physicians improved scores to 3.7 when starting with AI contours.

Conclusion

The proposed model achieved good quality IPA contours to improve uniformity of segmentation and to facilitate introduction of standardized IPA segmentation into clinical trials and practice.

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来源期刊
Physics and Imaging in Radiation Oncology
Physics and Imaging in Radiation Oncology Physics and Astronomy-Radiation
CiteScore
5.30
自引率
18.90%
发文量
93
审稿时长
6 weeks
期刊最新文献
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