Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region

数据集获取（Figshare）

• 源站标题：Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region
• DOI：10.1371/journal.pone.0133908
• 发布日期：2015-08-10
• 文件数：2 个
• 文件总体积：58.40 MB
• 源站页面：https://plos.figshare.com/articles/dataset/_Real_Time_Automatic_Segmentation_of_Optical_Coherence_Tomography_Volume_Data_of_the_Macular_Region_/1506996

命令行下载

curl -L -o "S1_Video.mp4" "https://ndownloader.figshare.com/files/2207134"
curl -L -o "S1_Data.zip" "https://ndownloader.figshare.com/files/2207135"

文件清单

• S1_Video.mp4（32.20 MB）
• S1_Data.zip（26.19 MB）

文件总体积 58.40 MB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Optical coherence tomography (OCT) is a high speed, high resolution and non-invasive imaging modality that enables the capturing of the 3D structure of the retina. The fast and automatic analysis of 3D volume OCT data is crucial taking into account the increased amount of patient-specific 3D imaging data. In this work, we have developed an automatic algorithm, OCTRIMA 3D (OCT Retinal IMage Analysis 3D), that could segment OCT volume data in the macular region fast and accurately. The proposed method is implemented using the shortest-path based graph search, which detects the retinal boundaries by searching the shortest-path between two end nodes using Dijkstra’s algorithm. Additional techniques, such as inter-frame flattening, inter-frame search region refinement, masking and biasing were introduced to exploit the spatial dependency between adjacent frames for the reduction of the processing time. Our segmentation algorithm was evaluated by comparing with the manual labelings and three state of the art graph-based segmentation methods. The processing time for the whole OCT volume of 496×644×51 voxels (captured by Spectralis SD-OCT) was 26.15 seconds which is at least a 2-8-fold increase in speed compared to other, similar reference algorithms used in the comparisons. The average unsigned error was about 1 pixel (∼ 4 microns), which was also lower compared to the reference algorithms. We believe that OCTRIMA 3D is a leap forward towards achieving reliable, real-time analysis of 3D OCT retinal data.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0133908（source: https://plos.figshare.com/articles/dataset/_Real_Time_Automatic_Segmentation_of_Optical_Coherence_Tomography_Volume_Data_of_the_Macular_Region_/1506996）。