Liver tumor segmentation from computed tomography images using multiscale residual dilated encoder‐decoder network

In this paper, an encoder‐decoder‐based architecture, which segments liver tumors with a two‐step training process is proposed. Accurate liver tumor segmentation from CT images is still a major problem that impacts the diagnosis process. Heterogeneous densities, shapes, and unclear boundaries make t...

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Veröffentlicht in:	International journal of imaging systems and technology Jg. 32; H. 2; S. 600 - 613
Hauptverfasser:	Tummala, Bindu Madhavi, Barpanda, Soubhagya Sankar
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken, USA John Wiley & Sons, Inc 01.03.2022 Wiley Subscription Services, Inc
Schlagworte:	Coders Computed tomography Data loss Datasets dilated convolutions encoder‐decoder architecture Image segmentation Liver liver tumor segmentation Medical imaging semantic segmentation Training Tumors
ISSN:	0899-9457, 1098-1098
Online-Zugang:	Volltext
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