Prof. Jinsil Seong Profile

Prof. Jinsil Seong

at Yonsei Univ College of Medicine

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Publications (2)

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Proceedings Article | 16 March 2020 Paper

Survival prediction of liver cancer patients from CT images using deep learning and radiomic feature-based regression

Hansang Lee, Helen Hong, Jinsil Seong, Jin Sung Kim, Junmo Kim

Proceedings Volume 11314, 113143L (2020) https://doi.org/10.1117/12.2551349

KEYWORDS: Computed tomography, Tumors, Feature extraction, Binary data, Liver cancer, Machine learning, Radiotherapy, Cancer, Oncology, Statistical analysis

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Prediction of survival period for patients with hepatocellular carcinoma (HCC) provides important information for treatment planning such as radiotherapy. However, the task is known to be challenging due to the similarity of tumor imaging characteristics from patients with different survival periods. In this paper, we propose a survival prediction method using deep learning and radiomic features from CT images with support vector machine (SVM) regression. First, to extract the deep features, the convolutional neural network (CNN) is trained for the task of classifying the patients for 24-month survival. Second, the radiomic features including texture and shape are extracted from the patient images. After concatenating the radiomic features and the deep features, the SVM regressor is trained to predict the survival period of the patients. The experiment was performed on the CT scans of 171 HCC patients with 5-fold cross validation. In the experiments, the proposed method showed an accuracy of 86.5%, a root-mean-squared-error (RMSE) of 11.6, and a Spearman rank coefficient of 0.11. In comparisons with the deep feature-only- and radiomic feature-only regression results, the proposed method showed improved accuracy and RMSE than both, but lower rank coefficient than the radiomic feature-only regression. It can be observed that (1) the deep learning of CT images has a promising potential for predicting the survival period of HCC patients, and (2) the radiomic feature analysis provides useful information to strengthen the power of deep learning-based survival prediction.

Proceedings Article | 16 March 2020 Paper

Automatic liver segmentation in abdominal CT images using combined 2.5D and 3D segmentation networks with high-score shape prior for radiotherapy treatment planning

Julip Jung, Helen Hong, Taesik Jeong, Jinsil Seong, Jin Sung Kim

Proceedings Volume 11314, 113143H (2020) https://doi.org/10.1117/12.2551287

KEYWORDS: Liver, Image segmentation, Computed tomography, 3D image processing, Radiotherapy, Abdomen

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Liver segmentation is a prerequisite for measuring hepatic volume in liver transplantation, modeling of the liver anatomy in hepatic surgery planning, and contouring in radiotherapy treatment planning. The main challenges of liver segmentation are the appearance similarity of liver and surrounding stomach, heart, and spleen in 2D images and are the large shape variations of liver in 3D volume. Therefore, we propose a deep learning-based liver segmentation method by using global context of three orthogonal planes to localize the liver in whole abdomen and by using local context of targeted liver bounding volume and high-score shape prior to delineate the liver without leakage to the surrounding structures. To localize the liver within the whole abdomen and exclude outliers through the global context, three 2D segmentation networks are learned on each axial, coronal, and sagittal planes. To consider the shape information obtained from the 2D segmentation network in the next 3D segmentation network, the high-score shape prior is generated by a weighted fusion of three score maps. To correct the fine details of the liver in the targeted liver bounding volume and to be less affected by shape variation, the 3D segmentation network is learned based on 3D U-Net with highscore shape prior. Experimental results show that the DSC of the proposed segmentation network with high-score shape prior (LiverNet-WS) was 94.3%, which is 5.4% higher than LiverNet without high-score shape prior. The proposed method accurately localized the liver within the whole abdomen by using global contexts of three orthogonal planes. Moreover, segmentation accuracy improved fine details considering local context and avoided over-segmentation considering high-score shape prior.

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