Bidirectional Recurrence for Cardiac Motion Tracking with Gaussian Process Latent Coding
Authors: Jiewen Yang, Yiqun Lin, Bin Pu, Xiaomeng Li
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the performance of GPTrack in cardiac motion tracking, we conduct experiments based on 3D Echocardiogram videos [17, 18] and 4D temporal MRI image [19]. Results in Tables 1, 2 and 3, show the GPTrack enhance the accuracy of motion tracking performance in a clear margin, without substantially increasing the computational cost in comparison to other state-of-the-art methods. |
| Researcher Affiliation | Academia | Jiewen Yang Yiqun Lin Bin Pu Xiaomeng Li B The Hong Kong University of Science and Technology {jyangcu, ylindw}@connect.ust.hk {eebinpu, eexmli}@ust.hk |
| Pseudocode | No | The paper describes its methodology using mathematical formulations and architectural diagrams (Figures 2 and 3), but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at: https://github.com/xmed-lab/GPTrack. |
| Open Datasets | Yes | Cardiac UDA [17]. The Cardiac UDA dataset collected from two medical centers consists of 314 echocardiogram videos from patients. CAMUS [18]. The CAMUS dataset provides pixel-level annotations for the left ventricle, myocardium, and left atrium in the Apical two-chamber view... ACDC [19]. The ACDC dataset consists of 100 4D temporal cardiac MRI cases. |
| Dataset Splits | Yes | In Cardiac UDA, we split the dataset into 8 : 2 for training and validation. During testing, we reported results in 10 fully annotated videos. In the CAMUS [18] dataset, videos without annotation are used for only training, while we randomly split the remaining 450 annotated videos into 300/50/100 for training, validation and testing. |
| Hardware Specification | Yes | For all experiments, We use Intel(R) Xeon(R) Platinum 8375C with 1 RTX3090 for both training and inference. |
| Software Dependencies | No | We trained the model using the Adam optimizer with betas equal to 0.9 and 0.99. |
| Experiment Setup | Yes | We trained the model using the Adam optimizer with betas equal to 0.9 and 0.99. The training batch size of the model was set to 1. We trained for a total of 1000 epochs with an initial learning rate of 5e 4 and decay by a factor of 0.5 in every 50 epochs. During training, for Cardiac UDA [17] and CAMUS [18], we resized each frame to 384 384 and then randomly cropped them to 256 256. All frames were normalized to [0,1] during training. In temporal augmentation of datasets [17, 18], we randomly selected 32 frames from an echocardiogram video with a sampling ratio of either 1 or 2. For ACDC [19], we resampled all scans with a voxel spacing of 1.5 1.5 3.15mm and cropped them to 128 128 32, normalized the intensity of all images to [-1, 1]. For spatial data augmentation of all datasets, we randomly applied flipping, rotation and Gaussian blurring. |