Automated segmentation of colorectal liver metastasis and liver ablation on contrast-enhanced CT images.

Anderson, Brian M; Rigaud, Bastien; Lin, Yuan-Mao; Jones, A Kyle; Kang, HynSeon Christine; Odisio, Bruno C; Brock, Kristy K

Anderson, Brian M; Rigaud, Bastien; Lin, Yuan-Mao; Jones, A Kyle; Kang, HynSeon Christine; Odisio, Bruno C; Brock, Kristy K.

Afiliación

Anderson BM; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Rigaud B; UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Lin YM; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Jones AK; Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Kang HC; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Odisio BC; Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Brock KK; Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.

Front Oncol ; 12: 886517, 2022.

Article en En | MEDLINE | ID: mdl-36033508

Palabras clave

biomechanical modeling; computed tomography; deep-learning; liver cancer; percutaneous ablation

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Qualitative_research Idioma: En Revista: Front Oncol Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google