Papers › Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions
Reza Azad, Maryam Asadi-Aghbolaghi, Mahmood Fathy, Sergio Escalera
In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. Finally, we can accelerate the convergence speed of the proposed network by employing batch normalization (BN). The proposed model is evaluated on three datasets of: retinal blood vessel segmentation, skin lesion segmentation, and lung nodule segmentation, achieving state-of-the-art performance.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Lesion Segmentation | ISIC 2018 | BCDU-net | mean Dice | 0.847 | #14 of 17 | Archive leaderboard | report |
| Lesion Segmentation | ISIC 2018 | BCDU-Net (d=3) | F1-Score | 0.851 | #17 of 17 | Archive leaderboard | report |
| Lung Nodule Segmentation | LUNA | BCDU-Net (d=3) | AUC | 0.9946 | #1 of 5 | Archive leaderboard | report |
| Lung Nodule Segmentation | LUNA | BCDU-Net (d=3) | F1 score | 0.9904 | #1 of 5 | Archive leaderboard | report |
| Lung Nodule Segmentation | Lung Nodule | BCDU-net | Dice Score | 0.994 | #1 of 1 | Archive leaderboard | report |
| Medical Image Segmentation | DRIVE | BCDU-net | F1 score | 0.8222 | #5 of 5 | Archive leaderboard | report |
| Retinal Vessel Segmentation | DRIVE | BCDU-Net (d=3) | AUC | 0.9789 | #13 of 22 | Archive leaderboard | report |
| Retinal Vessel Segmentation | DRIVE | BCDU-Net (d=3) | F1 score | 0.8224 | #13 of 22 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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