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Automatic Extraction of Liver Region from Medical Images by Using an MFUnet

이귀상|양형정|김수형|VO THI TUONG VI|오아란
(사)한국스마트미디어학회 (Korean Institute of Smart Media)|2020.09|스마트미디어저널 (Smart Media Journal)|제9권 제3호|pp. 59-70|DOI http://dx.doi.org/10.30693/smj.2020.9.3.59

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키워드

CT imagesliver segmentationmedial image
주제분류컴퓨터학

초록

This paper presents a fully automatic tool to recognize the liver region from CT images based on a deep learning model, namely Multiple Filter U-net, MFUnet. The advantages of both U-net and Multiple Filters were utilized to construct an autoencoder model, called MFUnet for segmenting the liver region from computed tomograph. The MFUnet architecture includes the autoencoding model which is used for regenerating the liver region, the backbone model for extracting features which is trained on ImageNet, and the predicting model used for liver segmentation. The LiTS dataset and Chaos dataset were used for the evaluation of our research. This result shows that the integration of Multiple Filter to U-net improves the performance of liver segmentation and it opens up many research directions in medical imaging processing field.

Abstract

This paper presents a fully automatic tool to recognize the liver region from CT images based on a deep learning model, namely Multiple Filter U-net, MFUnet. The advantages of both U-net and Multiple Filters were utilized to construct an autoencoder model, called MFUnet for segmenting the liver region from computed tomograph. The MFUnet architecture includes the autoencoding model which is used for regenerating the liver region, the backbone model for extracting features which is trained on ImageNet, and the predicting model used for liver segmentation. The LiTS dataset and Chaos dataset were used for the evaluation of our research. This result shows that the integration of Multiple Filter to U-net improves the performance of liver segmentation and it opens up many research directions in medical imaging processing field.

참고문헌 (32)

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관련 논문AI추천

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