Abstract：
BACKGROUND: The volume of the implant is the most critical element of breast reconstruction, so it is necessary to accurately assess the preoperative volume of the healthy and affected breasts and select the appropriate implant for placement. Accurate and automated methods for quantitative assessment of breast volume can optimize breast reconstruction surgery and assist physicians in clinical decision making. The aim of this study was to develop an artificial intelligence model for automated segmentation of the breast and measurement of volume.
METHODS: A total of 249 subjects undergoing breast reconstruction surgery were enrolled in this study. Subjects underwent preoperative breast MRI, and the breast region manually outlined by the imaging physician served as the gold standard for volume measurement by the automated segmentation model. In this study, we developed three automated algorithms for automatic segmentation of breast regions, including a simple alignment model, an alignment dynamic encoding model, and a deep learning model. The volumetric agreement between the three automated segmentation algorithms and the breast regions manually segmented by imaging physicians was evaluated by calculating the mean square error (MSE) and intragroup correlation coefficient (ICC), and the reproducibility of the automated segmentation of the breast regions was assessed by the test-retest step.
RESULTS: The three breast automated segmentation models developed in this study (simple registration model, dynamic programming model, and deep learning model) showed strong ICC with manual segmentation of the breast region, with MSEs of 1.124, 0.693, and 0.781, and ICCs of 0.975 (95% CI, 0.869-0.991), 0.986 (95% CI, 0.967-0.996), and 0.983 (95% CI, 0.961-0.992), respectively. Regarding the test-retest results of breast volume, the dynamic programming model performed the best with an MSE of 0.370 and an ICC of 0.993 (95% CI, 0.982-0.997), followed by the deep learning algorithm with an MSE of 0.741 and an ICC of 0.983 (95% CI, 0.956-0.993), and the simple registration algorithm with an MSE of 0.763 and an ICC of 0.982 (95% CI, 0.949-0.993). The reproducibility of the breast region segmented by the three automated algorithms was higher than that of manual segmentation by different radiologists.
CONCLUSIONS: The three automated breast segmentation algorithms developed in this study generate accurate and reliable breast regions, enable highly reproducible breast region segmentation and automated volume measurements, and provide a valuable tool for surgical selection of appropriate prostheses.
METHODS: This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
METHODS: A total of 249 subjects undergoing breast reconstruction surgery were enrolled in this study. Subjects underwent preoperative breast MRI, and the breast region manually outlined by the imaging physician served as the gold standard for volume measurement by the automated segmentation model. In this study, we developed three automated algorithms for automatic segmentation of breast regions, including a simple alignment model, an alignment dynamic encoding model, and a deep learning model. The volumetric agreement between the three automated segmentation algorithms and the breast regions manually segmented by imaging physicians was evaluated by calculating the mean square error (MSE) and intragroup correlation coefficient (ICC), and the reproducibility of the automated segmentation of the breast regions was assessed by the test-retest step.
RESULTS: The three breast automated segmentation models developed in this study (simple registration model, dynamic programming model, and deep learning model) showed strong ICC with manual segmentation of the breast region, with MSEs of 1.124, 0.693, and 0.781, and ICCs of 0.975 (95% CI, 0.869-0.991), 0.986 (95% CI, 0.967-0.996), and 0.983 (95% CI, 0.961-0.992), respectively. Regarding the test-retest results of breast volume, the dynamic programming model performed the best with an MSE of 0.370 and an ICC of 0.993 (95% CI, 0.982-0.997), followed by the deep learning algorithm with an MSE of 0.741 and an ICC of 0.983 (95% CI, 0.956-0.993), and the simple registration algorithm with an MSE of 0.763 and an ICC of 0.982 (95% CI, 0.949-0.993). The reproducibility of the breast region segmented by the three automated algorithms was higher than that of manual segmentation by different radiologists.
CONCLUSIONS: The three automated breast segmentation algorithms developed in this study generate accurate and reliable breast regions, enable highly reproducible breast region segmentation and automated volume measurements, and provide a valuable tool for surgical selection of appropriate prostheses.
METHODS: This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
摘要：
背景：植入物的体积是乳房重建的最关键要素，因此，有必要准确评估健康和受影响的乳房的术前体积，并选择合适的植入物进行放置。用于定量评估乳房体积的准确和自动化方法可以优化乳房重建手术并帮助医生进行临床决策。这项研究的目的是开发一种人工智能模型，用于自动分割乳房和测量体积。
方法：本研究共纳入249名接受乳房再造手术的受试者。受试者术前接受乳腺MRI检查，并且由成像医师手动勾勒出的乳房区域作为通过自动分割模型进行体积测量的金标准。在这项研究中,我们开发了三种自动分割乳房区域的自动算法，包括一个简单的对齐模型，对齐动态编码模型，和深度学习模型。通过计算均方误差（MSE）和组内相关系数（ICC）来评估三种自动分割算法与影像医师手动分割的乳房区域之间的体积一致性。并且通过测试-重测步骤评估乳房区域自动分割的可重复性。
结果：本研究开发的三种乳房自动分割模型（简单配准模型，动态规划模型,和深度学习模型)显示出强大的ICC，手动分割乳腺区域，MSEs为1.124、0.693和0.781，ICC为0.975（95%CI，0.869-0.991），0.986（95%CI，0.967-0.996），和0.983（95%CI，0.961-0.992），分别。关于乳房体积的重测结果，动态规划模型表现最好，MSE为0.370，ICC为0.993(95%CI，0.982-0.997)，其次是深度学习算法，MSE为0.741，ICC为0.983(95%CI，0.956-0.993)，和简单的配准算法，MSE为0.763，ICC为0.982（95％CI，0.949-0.993）。三种自动算法分割的乳房区域的再现性高于不同放射科医师的手动分割。
结论：本研究中开发的三种自动乳房分割算法可生成准确可靠的乳房区域，实现高度可重复的乳房区域分割和自动体积测量，并为手术选择合适的假体提供了有价值的工具。
方法：本期刊要求作者为每个提交的证据分配一个级别，该级别的证据适用于循证医学排名。这不包括评论文章，书评,和有关基础科学的手稿，动物研究,尸体研究，和实验研究。对于这些循证医学评级的完整描述，请参阅目录或在线作者说明www。springer.com/00266.
方法：本研究共纳入249名接受乳房再造手术的受试者。受试者术前接受乳腺MRI检查，并且由成像医师手动勾勒出的乳房区域作为通过自动分割模型进行体积测量的金标准。在这项研究中,我们开发了三种自动分割乳房区域的自动算法，包括一个简单的对齐模型，对齐动态编码模型，和深度学习模型。通过计算均方误差（MSE）和组内相关系数（ICC）来评估三种自动分割算法与影像医师手动分割的乳房区域之间的体积一致性。并且通过测试-重测步骤评估乳房区域自动分割的可重复性。
结果：本研究开发的三种乳房自动分割模型（简单配准模型，动态规划模型,和深度学习模型)显示出强大的ICC，手动分割乳腺区域，MSEs为1.124、0.693和0.781，ICC为0.975（95%CI，0.869-0.991），0.986（95%CI，0.967-0.996），和0.983（95%CI，0.961-0.992），分别。关于乳房体积的重测结果，动态规划模型表现最好，MSE为0.370，ICC为0.993(95%CI，0.982-0.997)，其次是深度学习算法，MSE为0.741，ICC为0.983(95%CI，0.956-0.993)，和简单的配准算法，MSE为0.763，ICC为0.982（95％CI，0.949-0.993）。三种自动算法分割的乳房区域的再现性高于不同放射科医师的手动分割。
结论：本研究中开发的三种自动乳房分割算法可生成准确可靠的乳房区域，实现高度可重复的乳房区域分割和自动体积测量，并为手术选择合适的假体提供了有价值的工具。
方法：本期刊要求作者为每个提交的证据分配一个级别，该级别的证据适用于循证医学排名。这不包括评论文章，书评,和有关基础科学的手稿，动物研究,尸体研究，和实验研究。对于这些循证医学评级的完整描述，请参阅目录或在线作者说明www。springer.com/00266.
