RESEARCH ARTICLE
Liver tumor segmentation from computed tomography images using multiscale residual dilated encoder-decoder network
Bindu Madhavi Tummala,
Soubhagya Sankar Barpanda,
Bindu Madhavi Tummala
School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India
Search for more papers by this authorCorresponding Author
Soubhagya Sankar Barpanda
School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India
Correspondence
Soubhagya Sankar Barpanda, School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India.
Email: [email protected]
Search for more papers by this authorBindu Madhavi Tummala,
Soubhagya Sankar Barpanda,
Bindu Madhavi Tummala
School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India
Search for more papers by this authorCorresponding Author
Soubhagya Sankar Barpanda
School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India
Correspondence
Soubhagya Sankar Barpanda, School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India.
Email: [email protected]
Search for more papers by this authorAbstract
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 tumor extraction challenging. First, the proposed network segments the liver, and then tumors are extracted from the liver ROIs. We have scaled down the images into different resolutions at each scale and applied normal convolutions along with the dilations and residual connections to capture broad conceptual information without data loss. MDICE, a combined loss function is used to enhance the learning capability and the 3D-IRCADb1 dataset is considered for training and testing because of its tumor complexities. The segmentation quality metrics DICE, MDICE are analyzed on the 3D-IRCADb1 dataset and obtained 0.98 and 0.65 accuracies per case for liver and tumor segmentation respectively, and found improvement over U-Net and other variants.
Open Research
DATA AVAILABILITY STATEMENT
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
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