Automated Segmentation of Overlapping Cytoplasm in Cervical Smear Images via Contour Fragments
Authors: Youyi Song, Jing Qin, Baiying Lei, Kup-Sze Choi
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using two cervical smear datasets, the performance of our method is extensively evaluated and compared with that of the stateof-the-art approaches; the results show the superiority of the proposed method. |
| Researcher Affiliation | Academia | Center for Smart Health, School of Nursing, The Hong Kong Polytechnic University National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, China |
| Pseudocode | No | The paper describes its algorithms and methods in detail using text and mathematical equations, but it does not include any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about making its source code publicly available or a link to a code repository. |
| Open Datasets | Yes | This dataset is obtained from the website of ISBI 2015 Overlapping Cervical Cytology Image Segmentation Challenge. |
| Dataset Splits | No | All these four parameters are optimized by the cross validation procedure on a small training dataset, and our experimental findings of these parameters are follows. |
| Hardware Specification | Yes | All experiments are conducted on a PC with a 2.20 GHz Intel Core i5 CPU and 4.00 GB of RAM, and they are all implemented in MATLAB. |
| Software Dependencies | No | The paper states that experiments are 'implemented in MATLAB' but does not specify the version of MATLAB or any other software dependencies with version numbers. |
| Experiment Setup | Yes | When using a small value of penalty rate ξ, the role of this energy term is diminished; however, when using a large value of it, it is more likely to lead to turbulence of graph s energy, so that graph s convergence cannot be reached. It is set as 1.5 in both datasets. The value of ω roughly depends on the overlapping degree in the dataset; the higher overlapping degree, the larger value. In our experiments, we set it to 7 in the Pap stain dataset, and to 10 in the H&E dataset. The values of ω1 and ω2 rely on imaging quality and overlapping degree. When images with high imaging quality and low overlapping degree, they both should be set a larger value; otherwise, curvature s role should be stressed greater. They are set as 1 and 0.2 in the Pap dataset, and as 0.7 and 0.4 in the H&E dataset, respectively. |