nnU-Net based segmentation and 3D reconstruction of uterine fibroids with MRI images for HIFU surgery planning.

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING BMC Medical Imaging Pub Date : 2024-09-06 DOI:10.1186/s12880-024-01385-3
Ting Wang, Yingang Wen, Zhibiao Wang
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Abstract

High-Intensity Focused Ultrasound (HIFU) ablation represents a rapidly advancing non-invasive treatment modality that has achieved considerable success in addressing uterine fibroids, which constitute over 50% of benign gynecological tumors. Preoperative Magnetic Resonance Imaging (MRI) plays a pivotal role in the planning and guidance of HIFU surgery for uterine fibroids, wherein the segmentation of tumors holds critical significance. The segmentation process was previously manually executed by medical experts, entailing a time-consuming and labor-intensive procedure heavily reliant on clinical expertise. This study introduced deep learning-based nnU-Net models, offering a cost-effective approach for their application in the segmentation of uterine fibroids utilizing preoperative MRI images. Furthermore, 3D reconstruction of the segmented targets was implemented to guide HIFU surgery. The evaluation of segmentation and 3D reconstruction performance was conducted with a focus on enhancing the safety and effectiveness of HIFU surgery. Results demonstrated the nnU-Net's commendable performance in the segmentation of uterine fibroids and their surrounding organs. Specifically, 3D nnU-Net achieved Dice Similarity Coefficients (DSC) of 92.55% for the uterus, 95.63% for fibroids, 92.69% for the spine, 89.63% for the endometrium, 97.75% for the bladder, and 90.45% for the urethral orifice. Compared to other state-of-the-art methods such as HIFUNet, U-Net, R2U-Net, ConvUNeXt and 2D nnU-Net, 3D nnU-Net demonstrated significantly higher DSC values, highlighting its superior accuracy and robustness. In conclusion, the efficacy of the 3D nnU-Net model for automated segmentation of the uterus and its surrounding organs was robustly validated. When integrated with intra-operative ultrasound imaging, this segmentation method and 3D reconstruction hold substantial potential to enhance the safety and efficiency of HIFU surgery in the clinical treatment of uterine fibroids.

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nnU-Net 基于磁共振成像的子宫肌瘤分割和三维重建,用于 HIFU 手术规划。
高强度聚焦超声消融术(HIFU)是一种快速发展的非侵入性治疗方式,在治疗占妇科良性肿瘤50%以上的子宫肌瘤方面取得了相当大的成功。术前磁共振成像(MRI)在 HIFU 治疗子宫肌瘤手术的计划和指导中起着关键作用,其中肿瘤的分割至关重要。以前的分割过程都是由医学专家手工完成的,耗时耗力,严重依赖临床专业知识。本研究引入了基于深度学习的 nnU-Net 模型,为其在利用术前核磁共振图像分割子宫肌瘤方面的应用提供了一种经济高效的方法。此外,还对分割后的目标进行了三维重建,以指导 HIFU 手术。对分割和三维重建性能进行评估的重点是提高 HIFU 手术的安全性和有效性。结果表明,nnU-Net 在分割子宫肌瘤及其周围器官方面的性能值得称赞。具体来说,3D nnU-Net的子宫骰子相似系数(DSC)为92.55%,子宫肌瘤为95.63%,脊柱为92.69%,子宫内膜为89.63%,膀胱为97.75%,尿道口为90.45%。与其他最先进的方法(如 HIFUNet、U-Net、R2U-Net、ConvUNeXt 和 2D nnU-Net)相比,3D nnU-Net 的 DSC 值明显更高,凸显了其卓越的准确性和稳健性。总之,三维 nnU-Net 模型在自动分割子宫及其周围器官方面的功效得到了有力的验证。如果与术中超声成像相结合,这种分割方法和三维重建在提高 HIFU 手术治疗子宫肌瘤的安全性和效率方面具有很大的潜力。
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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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