Deep learning-based segmentation of breast masses in dedicated breast CT imaging: Radiomic feature stability between radiologists and artificial intelligence.
Deep learning-based segmentation of breast masses in dedicated breast CT imaging: Radiomic feature stability between radiologists and artificial intelligence.
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DOI：
10.1016/j.compbiomed.2020.103629
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发表时间：
2020-03
影响因子：
7.7
通讯作者：
中科院分区：
文献类型：
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开发并验证了用于未增强专用乳腺CT图像中基于2D的乳腺肿块分割的深度学习（DL）网络，并研究了其与多名放射科医生手动注释相比在放射组学特征稳定性和诊断性能方面的鲁棒性。93个肿块样病变被广泛增强并用于训练网络（n = 58个肿块），然后使用符合性系数（值等于1表示完美性能），针对具有乳腺CT成像经验的合格乳腺放射科医生的手动地面实况进行测试（n = 35个肿块）。在计算机分割和4名放射科医生对35个测试集病例的注释之间研究了672个放射组描述符的稳定性和诊断能力。特征稳定性和诊断性能之间的良性和恶性的情况下进行了量化的区分，使用组内相关性（ICC）和多变量方差分析（MANOVA），进行每个分割的情况下（4放射科医生和DL算法）。基于DL的分割导致与注释的地面实况的一致性为0.85 ± 0.06。对于稳定性分析，尽管在由放射科医师执行的四个注释中发现了适度的一致性（一致性0.78 ± 0.03），但是发现超过90%的所有放射组学特征在多个分割中是稳定的（ICC> 0.75）。所有MANOVA分析均具有统计学显著性（p ≤ 0.05），所有维度均等于1，Wilks λ ≤ 0.35。总之，专用乳腺CT图像中基于DL的肿块分割可以实现高分割性能，并且证明在良性和恶性肿瘤的分类中提供稳定的放射组学描述符，其具有与专家放射科医师注释相当的区分能力。
A deep learning (DL) network for 2D-based breast mass segmentation in unenhanced dedicated breast CT images was developed and validated, and its robustness in radiomic feature stability and diagnostic performance compared to manual annotations of multiple radiologists was investigated. 93 mass-like lesions were extensively augmented and used to train the network (n=58 masses), which was then tested (n=35 masses) against manual ground truth of a qualified breast radiologist with experience in breast CT imaging using the Conformity coefficient (with a value equal to 1 indicating a perfect performance). Stability and diagnostic power of 672 radiomic descriptors were investigated between the computerized segmentation, and 4 radiologists’ annotations for the 35 test set cases. Feature stability and diagnostic performance in the discrimination between benign and malignant cases were quantified using intraclass correlation (ICC) and multivariate analysis of variance (MANOVA), performed for each segmentation case (4 radiologists and DL algorithm). DL-based segmentation resulted in a Conformity of 0.85±0.06 against the annotated ground truth. For the stability analysis, although modest agreement was found among the four annotations performed by radiologists (Conformity 0.78±0.03), over 90% of all radiomic features were found to be stable (ICC>0.75) across multiple segmentations. All MANOVA analyses were statistically significant (p≤0.05), with all dimensions equal to 1, and Wilks’ lambda ≤0.35. In conclusion, DL-based mass segmentation in dedicated breast CT images can achieve high segmentation performance, and demonstrated to provide stable radiomic descriptors with comparable discriminative power in the classification of benign and malignant tumors to expert radiologist annotation.
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DOI：
10.1016/0734-189x(89)90130-8
发表时间：
1989-02-01
期刊：
COMPUTER VISION GRAPHICS AND IMAGE PROCESSING
影响因子：
--
作者：
KELLER, JM;CHEN, S;CROWNOVER, RM
通讯作者：
CROWNOVER, RM
影响因子：
19.7
作者：
Drukker, Karen;Giger, Maryellen L.;Shepherd, John
通讯作者：
Shepherd, John
影响因子：
8
作者：
GUPTA, L;SRINATH, MD
通讯作者：
SRINATH, MD
DOI：
10.1109/tsmc.1973.4309314
发表时间：
1973-01-01
期刊：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS
影响因子：
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作者：
HARALICK, RM;SHANMUGAM, K;DINSTEIN, I
通讯作者：
DINSTEIN, I
DOI：
10.1016/j.ijmedinf.2018.06.003
发表时间：
2018-09-01
影响因子：
4.9
作者：
Al-antari, Mugahed A.;Al-masni, Mohammed A.;Kim, Tae-Seong
通讯作者：
Kim, Tae-Seong