A Mask R-CNN based Approach for Automatic Lung Segmentation in Computed Tomography Scans

Dandıl, Emre; Yildirim, Mehmet Suleyman

doi:10.1109/inista52262.2021.9548582

Cited by 3 publications

(1 citation statement)

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“…Hong et al [36] proposed an unsupervised domain adaptation approach for liver segmentation. A method using Mask R-CNN with soft parameter selection was proposed by Dandil et al [37]. Gong et al [38] introduced a fractional differential enhancement-based CNN model.…”

Section: Related Workmentioning

confidence: 99%

Transformer Skip‐Fusion Based SwinUNet for Liver Segmentation From CT Images

Kumar,

Vinod Kumar

2024

Int J Imaging Syst Tech

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Liver segmentation is a crucial step in medical image analysis and is essential for diagnosing and treating liver diseases. However, manual segmentation is time‐consuming and subject to variability among observers. To address these challenges, a novel liver segmentation approach, SwinUNet with transformer skip‐fusion is proposed. This method harnesses the Swin Transformer's capacity to model long‐range dependencies efficiently, the U‐Net's ability to preserve fine spatial details, and the transformer skip‐fusion's effectiveness in enabling the decoder to learn intricate features from encoder feature maps. In experiments using the 3DIRCADb and CHAOS datasets, this technique outperformed traditional CNN‐based methods, achieving a mean DICE coefficient of 0.988% and a mean Jaccard coefficient of 0.973% by aggregating the results obtained from each dataset, signifying outstanding agreement with ground truth. This remarkable accuracy in liver segmentation holds significant promise for improving liver disease diagnosis and enhancing healthcare outcomes for patients with liver conditions.

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Section: Related Workmentioning

confidence: 99%