耳蜗OCT数据集
包含5只豚鼠耳蜗OCT图像，173张标注切片，用于语义分割量化纤维化负担，研究人工耳蜗植入后残余听力损失。
基本信息
资源简介
该数据集包含来自5只豚鼠的OCT图像，共5个体积，每个体积1024个切片，但仅标注了173张切片，标注了ST/Free Space、CI/Track和Fibrosis三个类别。用于研究慢性人工耳蜗植入后耳蜗纤维化与残余听力损失的关系，任务为语义分割，旨在量化耳蜗纤维化负担。
下载信息
注册下载
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暂未开放千方医数集，医疗数据集部分，是为社区服务的公开医疗数据集搜索引擎，并不存储或者下载原始的任何数据。 如果您有其他医疗数据需求，可以和客服联系，或者下工单。我们有强大的三甲医疗机构帮助您提供个性化的医疗数据定制、采集、标注服务。
使用方式
数据集获取
git clone https://github.com/juliadietlmeier/CF-OCT-segmentation.git
curl -L -o repo.zip https://github.com/juliadietlmeier/CF-OCT-segmentation/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
CF-OCT-segmentation
This repository contains the code for the semantic segmentation and quantification of cochlear fibrosis (CF) in a novel Cochlear Fibrosis OCT dataset (animal models). We hope that our findings will advance future studies on exploring the relationship between cochlear fibrosis and residual hearing loss, development of cochlear implants (CI) or treatment of EAS-patients.
We benchmark different Keras segmentation models on our new Cochlear Fibrosis OCT dataset:
Proposed 2D-OCT-UNET
Fig. 1. Block diagram of the proposed 2D-OCT-UNET architecture for the multiclass OCT segmentation used in this work. The very deep 2D-
OCT-UNET processes two-dimensional OCT slices and consists of seven encoder-decoder blocks with skip connections. The numbers above the
encoder, bottleneck and decoder blocks indicate the number of filters in the convolutional Conv2D layers. GN stands for Group Normalization layers
with the number of groups parameter ng = 2. The input resolution of the UNET is set to be 1024 × 1024 pixels. We include Dropout(0.1) layers
only in the decoder. The number of filters in the last Conv2D layer is equal to the nc = 4 (number of classes).
Other models included are: VGG16-UNET, UEfficientNet, SegFormer and MST-DeepLabv3
To benchmark the SAM (Segment Anything Model) on the Cochlear OCT dataset we refer to the Github repository:
https://github.com/mazurowski-lab/finetune-SAM
Cochlear Fibrosis OCT Dataset
The open-source dataset can be dowloaded from the Open Science Framework (OSF)
Associated OSF project
https://osf.io/cghn7
Registration DOI
https://doi.org/10.17605/OSF.IO/WB5FS
Fig. 2. Cochlear Fibrosis OCT dataset samples and the corresponding ground truth annotations from five annotated volumes. The CI/Track class is depicted
in red, the Fibrosis class in green and the ST/Free Space class in blue.
Fig. 3. Samples from qualitative results are provided for each volume. First two rows: OCTV1L, Third row: OCTV7L, Fourth row: OCTV9L, Fifth
row: OCTV10L and the Sixth row: OCTV11L. By comparing to the Ground Truth (GT) it can be inferred that the 2D-OCT-UNET model performed the
best and the SAM (prompt-free auto-mode implementation with adapters) model qualitatively performed the worst. As pre
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。
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