Region-aware Global Context Modeling for Automatic Nerve Segmentation from Ultrasound Images
Authors: Huisi Wu, Jiasheng Liu, Wei Wang, Zhenkun Wen, Jing Qin2907-2915
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted extensive experiments on a famous public ultrasound nerve image segmentation dataset. Experimental results demonstrate that our method consistently outperforms our rivals in terms of segmentation accuracy. |
| Researcher Affiliation | Academia | 1College of Computer Science and Software Engineering, Shenzhen University 2Centre for Smart Health, The Hong Kong Polytechnic University |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | The code is available at https://github.com/jsonliu-szu/RAGCM. |
| Open Datasets | Yes | To evaluate the effectiveness of our proposed method, we conducted the experiments on the Kaggle ultrasound nerve segmentation challenge 1. This dataset is consists of 11143 ultrasound images with a resolution of 580 420, which are manually annotated by clinical experts to generate mask images. Among the 11143 samples, 4508 and 1127 images are used for training and validation respectively, while the rest images are used for testing. 1https://www.kaggle.com/c/ultrasound-nerve-segmentation |
| Dataset Splits | Yes | Among the 11143 samples, 4508 and 1127 images are used for training and validation respectively, while the rest images are used for testing. |
| Hardware Specification | Yes | We implemented our network by Pytorch (Paszke et al. 2017) on a 1 NVIDIA Ge Force RTX 2080TI (11GB memory). |
| Software Dependencies | No | The paper mentions 'Pytorch (Paszke et al. 2017)' but does not provide specific version numbers for Pytorch or any other software dependencies. |
| Experiment Setup | Yes | During the training process, our initial learning rate is 0.0001. To obtain a more smooth convergence curve, our learning rate is also multiplied by 1 iter total power with power = 0.9 after each iteration (Krogh and Hertz 1991). To speedup the network convergence, we also employed the Adam algorithm to optimize the training process. Considering that almost half of the images are background images, we also applied each mini-batch (batch size = 8) with a ratio of 1:1 of negative samples to positive samples to train our model. Our model can be converged after 70 epoches in our experiments. |