Modified U-Net (mU-Net) With Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images

• Published in: IEEE transactions on medical imaging Vol. 39; no. 5; pp. 1316 - 1325
• Main Authors: Seo, Hyunseok, Huang, Charles, Bassenne, Maxime, Xiao, Ruoxiu, Xing, Lei
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biomedical imaging; Blurring; Computed tomography; Convolution; Data mining; Deep learning; Feature extraction; Feature maps; Formability; Frequency analysis; Hepatocellular carcinoma; High resolution; Humans; Image analysis; Image processing; Image Processing, Computer-Assisted; Image reconstruction; Image segmentation; Liver; Liver cancer; Liver Neoplasms - diagnostic imaging; liver segmentation; Mathematical analysis; mU-Net; Organs; Radiation; Radiation therapy; Reproduction (copying); Tomography, X-Ray Computed; Tumors; U-Net

Segmentation of livers and liver tumors is one of the most important steps in radiation therapy of hepatocellular carcinoma. The segmentation task is often done manually, making it tedious, labor intensive, and subject to intra-/inter- operator variations. While various algorithms for delineating or...