Lung
Datasets
All datasets matching “Lung”4D-Lung
4D-Lung (segmentation subset)
Longitudinal 4D (respiratory-gated, phase-resolved) fan-beam CT of 20
locally-advanced non-small-cell lung cancer (NSCLC) patients, with expert
manual RTSTRUCT contours, from Data from 4D Lung Imaging of NSCLC Patients
(4D-Lung) on The Cancer Imaging Archive (Hugo et al., VCU).
This is the segmentable subset of the full collection — read carefully.
The full TCIA 4D-Lung collection is 183 GB and contains both 4D fan-beam CT
(4D-FBCT, "4DCT") and 4D… See the full description on the dataset page: https://huggingface.co/datasets/MedOtter/4D-Lung.lung-tumour-study
Combining graph neural networks and computer vision methods for cell nuclei classification in lung tissue
This is the dataset of the article in the title. It contains 85 patches of 1024x1024 pixels from H&E stained WSIs of 9 different patients. It contains two main classes: tumoural (2) and non tumoural (1). Due to the difficulty of the problem, 153 cells were labelled as uncertain. For technical reasons, we decided to eliminate them in the train and validation set and we… See the full description on the dataset page: https://huggingface.co/datasets/Jerry-Master/lung-tumour-study.RIDER-LungCT-Seg
RIDER-LungCT-Seg
Manual primary gross-tumor-volume (GTV) segmentations of non-small-cell lung
cancer (NSCLC) on same-day test–retest chest CT, paired with their source
CT images, from RIDER-LungCT-Seg on The Cancer Imaging Archive (segmentations
from Aerts et al., Nat. Commun. 2014; base images from the RIDER "Coffee-break"
Lung CT collection, Zhao et al.).
This mirror is a precisely-isolated slice of a shared TCIA collection — read carefully.
On TCIA, the base "RIDER Lung CT"… See the full description on the dataset page: https://huggingface.co/datasets/MedOtter/RIDER-LungCT-Seg.QIN-LungCT-Seg
QIN-LungCT-Seg
QIN multi-site collection of Lung CT data with Nodule Segmentations — a TCIA
analysis result from the NCI Quantitative Imaging Network (QIN). Thoracic CT
scans of non-small-cell-lung-cancer (NSCLC) patients with multi-algorithm
nodule/tumor segmentations contributed by three institutions, each run three
times, as a study of inter-algorithm and test-retest segmentation variability
(Kalpathy-Cramer et al., J Digit Imaging 2016).
Read before benchmarking — three… See the full description on the dataset page: https://huggingface.co/datasets/MedOtter/QIN-LungCT-Seg.Lung_Nodule_Segmentation
Lung Nodule Segmentation Dataset
Introduction
Welcome to the Lung Nodule Segmentation Dataset repository! This project aims to provide a comprehensive dataset for researchers and developers to build and evaluate machine learning models for lung nodule segmentation. Accurate detection and segmentation of lung nodules are crucial steps in the early diagnosis and treatment of lung cancer.
Dataset Overview
The dataset consists of high-resolution CT scans with… See the full description on the dataset page: https://huggingface.co/datasets/basilshaji/Lung_Nodule_Segmentation.LungVis10
LungVis10
Licensed under CC BY 4.0.