OCT Daily Mar 13, 2017


  • 将 Relevance Vector Machine
  • 运用在乳腺组织的 OCT 成像结果
  • 的分类上。

Curated Paper

Visualization and tissue classification of human breast cancer images using ultrahigh-resolution OCT


Breast cancer is one of the leading cause of mortality in women. Optical coherence tomography (OCT) enables three dimensional visualization of biological tissue with micrometer level resolution at high speed, and can play an important role in early diagnosis and treatment guidance of breast cancer. In this study, we imaged human breast tissue using two spectral domain OCT systems at different wavelengths: a home-built ultra-high resolution (UHR) OCT system at 840nm (measured as 2.72 µm axial and 5.52 µm lateral) and a commercial OCT system at 1300nm with standard resolution (measured as 6.5 µm axial and 15 µm lateral). We found that detailed structures of basic units found in breast tissue, such as TDLUs, ducts, adipose and fibrous stroma, can be better delineated by UHR OCT. In addition, we added phyllodes, fibrotic focus and necrotic tumor to the UHR OCT image library of breast cancer. Moreover, by using regional features derived from OCT images produced by the two systems, we developed an automated classification algorithm based on relevance vector machine (RVM) to differentiate hollow-structured adipose tissue against solid tissue. We further developed B-scan based features for RVM to classify invasive ductal carcinoma (IDC) against normal fibrous stroma tissue amongst OCT datasets produced by the two systems. With a limited number of datasets, we showed that both OCT systems can achieve a good accuracy in identifying adipose tissue. Classification in UHR OCT images achieved higher sensitivity (94%) and specificity (93%) of adipose tissue than the sensitivity (91%) and specificity (76%) in 1300 nm OCT images. In IDC classification, similarly, we achieved better results with UHR OCT images, featured an overall accuracy of 84%, sensitivity of 89% and specificity of 71% in this preliminary study. Our work may open the door towards automatic intraoperative OCT evaluation of early-stage breast cancer.


Christine Hendon的实验室,虽然她当年压根没理我的信。Basic OCT scheme:


…adipose tissue has a characteristic honeycomb texture

Tissue classification algorithm flow/ 组织分类算法流程图如下:

定义灵敏度和确认度分析肿瘤结构,还跟Thorlabs OCT和传统病理学结果对比,部分成像结果如下:


In particular, ultra-high resolution (UHR) OCT provides images with better histological correlation.

What can I learn from

已经从对成像的改进进入到实用领域,像DeepMind这类公司利用Machine Learning 取代病理识别已经是趋势。该文章给我提供了一个流程,包括SBO LAB的文章1和FFOCT对乳腺癌的诊断2,试图解决这样一个问题:如何从成像结果变成可量化的结论。


All OCT images presented have a corresponding histology slides, which were annotated with the help of an experienced pathologist. The aspect ratio of UHR OCT images was scaled to match the dimension of the actual cross-sectional field of view in air (3 mm by 1.78 mm), and Thorlabs OCT images were presented in their original scale.

但目前如何以 Decent way 做组织病理学实验还需要专业病理科医生指导。

  1. Zarnescu, L. et al. Label-free characterization of vitrification-induced morphology changes in single-cell embryos with full-field optical coherence tomography. BIOMEDO 20, 096004-096004, doi:10.1117/1.JBO.20.9.096004 (2015).
  2. Assayag, O. et al. Large Field, High Resolution Full-Field Optical Coherence Tomography: A Pre-Clinical Study of Human Breast Tissue and Cancer Assessment. Technology in Cancer Research & Treatment 13, 455-468, doi:doi:10.7785/tcrtexpress.2013.600254 (2014).