2024/04/05 by Zhendong Zhang, Edward Robert Criscuolo, Zhang, Zhendong +5
Computer Science · Engineering · Medicine · #FOS: Electrical engineering #FOS: Physical sciences #Image and Video Processing (eess.IV) #Medical Image Segmentation Techniques #Medical Imaging and Analysis #Medical Physics (physics.med-ph) #Radiomics and Machine Learning in Medical Imaging #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2404.04427
openalex publication_date 2024/04/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Purpose: Evaluating deformable image registration (DIR) algorithms is vital for enhancing algorithm performance and gaining clinical acceptance. However, there's a notable lack of dependable DIR benchmark datasets for assessing DIR performance except for lung images. To address this gap, we aim to introduce our comprehensive liver computed tomography (CT) DIR landmark dataset library. Acquisition and Validation Methods: Thirty CT liver image pairs were acquired from several publicly available image archives as well as authors' institutions under institutional review board approval. The images were processed with a semi-automatic procedure to generate landmark pairs: 1) for each case, liver vessels were automatically segmented on one image; 2) landmarks were automatically detected at vessel bifurcations; 3) corresponding landmarks in the second image were placed using the deformable image registration method; 4) manual validation was applied to reject outliers and confirm the landmarks' positional accuracy. This workflow resulted in an average of ~68 landmark pairs per image pair, in a total of 2028 landmarks for all 30 cases. The general landmarking accuracy of this procedure was evaluated using digital phantoms. Estimates of the mean and standard deviation of landmark pair target registration errors (TRE) on digital phantoms were 0.64 and 0.40 mm. 99% of landmark pairs had TREs below 2 mm. Data Format and Usage Notes: All data are publicly available at Zenodo. Instructions for using our data and MATLAB code can be found on our GitHub page. Potential Applications: The landmark dataset generated in this work is the first collection of large-scale liver CT DIR landmarks prepared on real patient images. This dataset can provide researchers with a dense set of ground truth benchmarks for the quantitative evaluation of DIR algorithms within the liver.