segmentation masks  (Oxford Instruments)

Journal: Computer Methods and Programs in Biomedicine Update

Article Title: Robust lung segmentation in Chest X-ray images using modified U-Net with deeper network and residual blocks

doi: 10.1016/j.cmpbup.2025.100211

Figure Lengend Snippet: Fig. 1. Sample montgomery county chest x-ray images and their corresponding ground truth masks.

Article Snippet: The second dataset, the Shenzhen Hospital (SH) CXR Dataset [28] comprises 566 chest X-ray images with manually segmented ground truth masks obtained from Kaggle [29–32].

Techniques:

Journal: Computer Methods and Programs in Biomedicine Update

Article Title: Robust lung segmentation in Chest X-ray images using modified U-Net with deeper network and residual blocks

doi: 10.1016/j.cmpbup.2025.100211

Figure Lengend Snippet: Fig. 2. Sample shenzhen hospital chest x-ray images and their corresponding ground truth masks.

Article Snippet: The second dataset, the Shenzhen Hospital (SH) CXR Dataset [28] comprises 566 chest X-ray images with manually segmented ground truth masks obtained from Kaggle [29–32].

Techniques:

Journal: Computer Methods and Programs in Biomedicine Update

Article Title: Robust lung segmentation in Chest X-ray images using modified U-Net with deeper network and residual blocks

doi: 10.1016/j.cmpbup.2025.100211

Figure Lengend Snippet: Fig. 15. Sample MC predictions showing the segmented lung boundaries of both the ground truth masks (red) and the predicted one using the proposed DDRU-Net method (blue). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Article Snippet: The second dataset, the Shenzhen Hospital (SH) CXR Dataset [28] comprises 566 chest X-ray images with manually segmented ground truth masks obtained from Kaggle [29–32].

Techniques:

Journal: Computer Methods and Programs in Biomedicine Update

Article Title: Robust lung segmentation in Chest X-ray images using modified U-Net with deeper network and residual blocks

doi: 10.1016/j.cmpbup.2025.100211

Figure Lengend Snippet: Fig. 16. Sample failed SH predictions showing the segmented lung boundaries of both the ground truth masks (red) and the predicted one using the proposed DDRU-Net method (blue). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Article Snippet: The second dataset, the Shenzhen Hospital (SH) CXR Dataset [28] comprises 566 chest X-ray images with manually segmented ground truth masks obtained from Kaggle [29–32].

Techniques:

Journal: Computer Methods and Programs in Biomedicine Update

Article Title: Robust lung segmentation in Chest X-ray images using modified U-Net with deeper network and residual blocks

doi: 10.1016/j.cmpbup.2025.100211

Figure Lengend Snippet: Fig. 17. Sample successful SH predictions showing the segmented lung boundaries of both the ground truth masks (red) and the predicted one using the proposed DDRU-Net method (blue). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Article Snippet: The second dataset, the Shenzhen Hospital (SH) CXR Dataset [28] comprises 566 chest X-ray images with manually segmented ground truth masks obtained from Kaggle [29–32].

Techniques: