Automated breast mass detection in 3D reconstructed tomosynthesis volumes: a featureless approach.
Automated breast mass detection in 3D reconstructed tomosynthesis volumes: a featureless approach.
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3D 重建断层合成体积中的自动乳腺肿块检测：一种无特征的方法。
DOI：
10.1118/1.2953562
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发表时间：
2008
期刊：
影响因子：
3.8
通讯作者：
Lo,JosephY
中科院分区：
文献类型：
--
作者：
Singh,Swatee;Tourassi,GeorgiaD;Baker,JayA;Samei,Ehsan;Lo,JosephY
本研究的目的是提出并实现一个计算机辅助检测（CADe）乳腺断层合成工具。这项任务分两个阶段完成-高灵敏度质量检测器，然后是假阳性（FP）减少阶段。使用了来自100例人类受试者病例的乳腺断层合成数据，其中25例受试者有一个或多个肿块病变，其余正常。对于阶段1，通过网格搜索优化过滤器参数。对CADe识别的可疑位置进行重建，以产生感兴趣的3D CADe体积。第一阶段产生的最大灵敏度为93%，7.7 FP/乳房体积。与传统的CADe算法不同，在传统的CADe算法中，第二阶段的FP约简是通过特征提取和分析来完成的，而是使用信息论原理，互信息作为相似性度量。提出了三种方案，都使用留一例交叉验证抽样。A、B和C这三个方案的感兴趣区域知识库的组成不同。方案A的知识库由算法第一阶段生成的所有质量和FP ROI组成。方案B具有包含来自大量ROI的信息和随机提取的正常ROI的知识库。方案C的信息来自三个信息源--大众、FP和正常ROI。此外，性能作为知识库的组成的函数进行评估，根据系统达到最佳性能所需的FP或正常ROI的数量。结果表明，知识库需要不超过20倍的FP和30倍的正常ROI的群众，以达到最大的性能。最佳总体系统性能为85%灵敏度，方案A为每个乳房体积2.4 FP，方案B为每个乳房体积3.6 FP，方案C为每个乳房体积3 FP。
The purpose of this study was to propose and implement a computer aided detection (CADe) tool for breast tomosynthesis. This task was accomplished in two stages—a highly sensitive mass detector followed by a false positive (FP) reduction stage. Breast tomosynthesis data from 100 human subject cases were used, of which 25 subjects had one or more mass lesions and the rest were normal. For stage 1, filter parameters were optimized via a grid search. The CADe identified suspicious locations were reconstructed to yield 3D CADe volumes of interest. The first stage yielded a maximum sensitivity of 93% with 7.7 FPs/breast volume. Unlike traditional CADe algorithms in which the second stage FP reduction is done via feature extraction and analysis, instead information theory principles were used with mutual information as a similarity metric. Three schemes were proposed, all using leave‐one‐case‐out cross validation sampling. The three schemes, A, B, and C, differed in the composition of their knowledge base of regions of interest (ROIs). Scheme A's knowledge base was comprised of all the mass and FP ROIs generated by the first stage of the algorithm. Scheme B had a knowledge base that contained information from mass ROIs and randomly extracted normal ROIs. Scheme C had information from three sources of information—masses, FPs, and normal ROIs. Also, performance was assessed as a function of the composition of the knowledge base in terms of the number of FP or normal ROIs needed by the system to reach optimal performance. The results indicated that the knowledge base needed no more than 20 times as many FPs and 30 times as many normal ROIs as masses to attain maximal performance. The best overall system performance was 85% sensitivity with 2.4 FPs per breast volume for scheme A, 3.6 FPs per breast volume for scheme B, and 3 FPs per breast volume for scheme C.