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Lesion Segmentation in Automated 3D Breast Ultrasound: Volumetric Analysis
Ultrasonic Imaging ( IF 2.3 ) Pub Date : 2017-11-28 , DOI: 10.1177/0161734617737733
Richa Agarwal 1 , Oliver Diaz 1 , Xavier Lladó 1 , Albert Gubern-Mérida 2 , Joan C Vilanova 3 , Robert Martí 1
Affiliation  

Mammography is the gold standard screening technique in breast cancer, but it has some limitations for women with dense breasts. In such cases, sonography is usually recommended as an additional imaging technique. A traditional sonogram produces a two-dimensional (2D) visualization of the breast and is highly operator dependent. Automated breast ultrasound (ABUS) has also been proposed to produce a full 3D scan of the breast automatically with reduced operator dependency, facilitating double reading and comparison with past exams. When using ABUS, lesion segmentation and tracking changes over time are challenging tasks, as the three-dimensional (3D) nature of the images makes the analysis difficult and tedious for radiologists. The goal of this work is to develop a semi-automatic framework for breast lesion segmentation in ABUS volumes which is based on the Watershed algorithm. The effect of different de-noising methods on segmentation is studied showing a significant impact ( p < 0 . 05 ) on the performance using a dataset of 28 temporal pairs resulting in a total of 56 ABUS volumes. The volumetric analysis is also used to evaluate the performance of the developed framework. A mean Dice Similarity Coefficient of 0 . 69 ± 0 . 11 with a mean False Positive ratio 0 . 35 ± 0 . 14 has been obtained. The Pearson correlation coefficient between the segmented volumes and the corresponding ground truth volumes is r 2 = 0 . 960 ( p = 0 . 05 ). Similar analysis, performed on 28 temporal (prior and current) pairs, resulted in a good correlation coefficient r 2 = 0 . 967 ( p < 0 . 05 ) for prior and r 2 = 0 . 956 ( p < 0 . 05 ) for current cases. The developed framework showed prospects to help radiologists to perform an assessment of ABUS lesion volumes, as well as to quantify volumetric changes during lesions diagnosis and follow-up.

中文翻译:

自动 3D 乳腺超声中的病变分割:体积分析

乳房 X 光检查是乳腺癌筛查的金标准,但它对乳房致密的女性有一定的局限性。在这种情况下,通常推荐超声检查作为附加成像技术。传统的超声图产生乳房的二维 (2D) 可视化,并且高度依赖于操作员。还提出了自动乳房超声 (ABUS) 以自动生成乳房的完整 3D 扫描,减少对操作员的依赖,便于重复读取并与过去的检查进行比较。使用 ABUS 时,病变分割和跟踪随时间的变化是一项具有挑战性的任务,因为图像的三维 (3D) 特性使放射科医生的分析变得困难和乏味。这项工作的目标是开发一个基于分水岭算法的 ABUS 体积中乳房病变分割的半自动框架。研究了不同去噪方法对分割的影响,使用 28 个时间对的数据集对性能产生了显着影响 (p < 0 . 05),总共 56 个 ABUS 卷。体积分析也用于评估所开发框架的性能。平均骰子相似系数为 0 。69±0。11 平均误报率 0 。35±0。已获得14个。分割的体积与相应的地面实况体积之间的皮尔逊相关系数为 r 2 = 0 。960 (p = 0 . 05)。对 28 个时间(先前和当前)对进行的类似分析产生了良好的相关系数 r 2 = 0 。967 ( p < 0 . 05 ) 对于先验和 r 2 = 0 。956 ( p < 0 . 05 ) 对于当前病例。开发的框架显示出帮助放射科医生评估 ABUS 病灶体积以及量化病灶诊断和随访期间的体积变化的前景。
更新日期:2017-11-28
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