RESUMEN
Clinical imaging (e.g., magnetic resonance imaging and computed tomography) is a crucial adjunct for clinicians, aiding in the diagnosis of diseases and planning of appropriate interventions. This is especially true in malignant conditions such as hepatocellular carcinoma (HCC), where image segmentation (such as accurate delineation of liver and tumor) is the preliminary step taken by the clinicians to optimize diagnosis, staging, and treatment planning and intervention (e.g., transplantation, surgical resection, radiotherapy, PVE, embolization, etc). Thus, segmentation methods could potentially impact the diagnosis and treatment outcomes. This paper comprehensively reviews the literature (during the year 2012-2021) for relevant segmentation methods and proposes a broad categorization based on their clinical utility (i.e., surgical and radiological interventions) in HCC. The categorization is based on the parameters such as precision, accuracy, and automation.
Asunto(s)
Carcinoma Hepatocelular , Neoplasias Hepáticas , Carcinoma Hepatocelular/diagnóstico por imagen , Carcinoma Hepatocelular/cirugía , Humanos , Neoplasias Hepáticas/diagnóstico por imagen , Neoplasias Hepáticas/cirugía , Imagen por Resonancia Magnética , Tomografía Computarizada por Rayos XRESUMEN
This paper attempts to present a novel application of Binary Artificial Bat algorithm for more effective location management in cellular networks. The location management is a mobility management task, which involves tracking of the mobile stations to locate their exact positions so that an incoming call or data can be routed to the intended mobile user. The location management cost comprises of the costs incurred by two processes, namely location registration and location search. This work focuses on network cost optimization, using Binary Artificial Bat algorithm for reporting cell planning strategy, which has not been reported yet. Results of the proposed algorithm have been compared with that of Binary Particle Swarm Optimization (BPSO) and Binary Differential Evolution (BDE) for some reference and realistic networks. The proposed approach is found to perform as good as other state-of-art techniques reported in the literature in terms of accuracy in solution, but it shows perceptible improvement in convergence speed.