Deep learning assisted detection and segmentation of uterine fibroids using multi-orientation magnetic resonance imaging

IF 2.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Xin-Yu Liu, Zhi-Lin Yuan, Fu-Ze Cong, Li Mao, Xiu-Li Li, Zhen Zhou, Jing Ren, Yuan Li, Yan Zhang, Yong-Lan He, Hua-Dan Xue, Zheng-Yu Jin
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引用次数: 0

Abstract

Purpose

To develop deep learning models for automated detection and segmentation of uterine fibroids using multi-orientation MRI.

Methods

Pre-treatment sagittal and axial T2-weighted MRI scans acquired from patients diagnosed with uterine fibroids were collected. The proposed segmentation models were constructed based on the three-dimensional nnU-Net framework. Fibroid detection efficacy was assessed, with subgroup analyses by size and location. The segmentation performance was evaluated using Dice similarity coefficients (DSCs), 95% Hausdorff distance (HD95), and average surface distance (ASD).

Results

The internal dataset comprised 299 patients who were divided into the training set (n = 239) and the internal test set (n = 60). The external dataset comprised 45 patients. The sagittal T2WI model and the axial T2WI model demonstrated recalls of 74.4%/76.4% and precision of 98.9%/97.9% for fibroid detection in the internal test set. The models achieved recalls of 93.7%/95.3% for fibroids ≥ 4 cm. The recalls for International Federation of Gynecology and Obstetrics (FIGO) type 2–5, FIGO types 0\1\2(submucous), fibroids FIGO types 5\6\7(subserous) were 100%/100%, 73.3%/78.6%, and 80.3%/81.9%, respectively. The proposed models demonstrated good performance in segmentation of the uterine fibroids with mean DSCs of 0.789 and 0.804, HD95s of 9.996 and 10.855 mm, and ASDs of 2.035 and 2.115 mm in the internal test set, and with mean DSCs of 0.834 and 0.818, HD95s of 9.971 and 11.874 mm, and ASDs of 2.031 and 2.273 mm in the external test set.

Conclusion

The proposed deep learning models showed promise as reliable methods for automating the detection and segmentation of the uterine fibroids, particularly those of clinical relevance.

Graphical abstract

Abstract Image

Abstract Image

深度学习辅助子宫肌瘤多方向磁共振成像检测与分割。
目的:建立基于多方向MRI的子宫肌瘤自动检测和分割的深度学习模型。方法:收集诊断为子宫肌瘤的患者治疗前矢状和轴向t2加权MRI扫描。基于三维nnU-Net框架构建了所提出的分割模型。评估肌瘤的检测效果,并根据大小和位置进行亚组分析。使用Dice相似系数(dsc)、95% Hausdorff距离(HD95)和平均表面距离(ASD)来评估分割性能。结果:内部数据集包括299例患者,分为训练集(n = 239)和内部测试集(n = 60)。外部数据集包括45名患者。矢状位T2WI和轴位T2WI模型对肌瘤的检出召回率分别为74.4%/76.4%,准确率分别为98.9%/97.9%。对于≥4 cm的肌瘤,模型的召回率为93.7%/95.3%。国际妇产科学联合会(FIGO) 2-5型、FIGO 0\1\2型(粘液下)、FIGO 5\6\7型(粘液下)的召回率分别为100%/100%、73.3%/78.6%、80.3%/81.9%。所建立的模型在子宫肌瘤分割中表现出较好的分割效果,内组平均dsc分别为0.789和0.804,hd95分别为9.996和10.855 mm, asd分别为2.035和2.115 mm;外组平均dsc分别为0.834和0.818,hd95分别为9.971和11.874 mm, asd分别为2.031和2.273 mm。结论:所提出的深度学习模型有望成为子宫肌瘤自动化检测和分割的可靠方法,特别是那些具有临床意义的方法。
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来源期刊
Abdominal Radiology
Abdominal Radiology Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
5.20
自引率
8.30%
发文量
334
期刊介绍: Abdominal Radiology seeks to meet the professional needs of the abdominal radiologist by publishing clinically pertinent original, review and practice related articles on the gastrointestinal and genitourinary tracts and abdominal interventional and radiologic procedures. Case reports are generally not accepted unless they are the first report of a new disease or condition, or part of a special solicited section. Reasons to Publish Your Article in Abdominal Radiology: · Official journal of the Society of Abdominal Radiology (SAR) · Published in Cooperation with: European Society of Gastrointestinal and Abdominal Radiology (ESGAR) European Society of Urogenital Radiology (ESUR) Asian Society of Abdominal Radiology (ASAR) · Efficient handling and Expeditious review · Author feedback is provided in a mentoring style · Global readership · Readers can earn CME credits
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