PDF(Pubmed)
Abstract：
UNASSIGNED: Accurately detecting and segmenting areas of retinal atrophy are paramount for early medical intervention in pathological myopia (PM). However, segmenting retinal atrophic areas based on a two-dimensional (2D) fundus image poses several challenges, such as blurred boundaries, irregular shapes, and size variation. To overcome these challenges, we have proposed an attention-aware retinal atrophy segmentation network (ARA-Net) to segment retinal atrophy areas from the 2D fundus image.
UNASSIGNED: In particular, the ARA-Net adopts a similar strategy as UNet to perform the area segmentation. Skip self-attention connection (SSA) block, comprising a shortcut and a parallel polarized self-attention (PPSA) block, has been proposed to deal with the challenges of blurred boundaries and irregular shapes of the retinal atrophic region. Further, we have proposed a multi-scale feature flow (MSFF) to challenge the size variation. We have added the flow between the SSA connection blocks, allowing for capturing considerable semantic information to detect retinal atrophy in various area sizes.
UNASSIGNED: The proposed method has been validated on the Pathological Myopia (PALM) dataset. Experimental results demonstrate that our method yields a high dice coefficient (DICE) of 84.26%, Jaccard index (JAC) of 72.80%, and F1-score of 84.57%, which outperforms other methods significantly.
UNASSIGNED: Our results have demonstrated that ARA-Net is an effective and efficient approach for retinal atrophic area segmentation in PM.
UNASSIGNED: In particular, the ARA-Net adopts a similar strategy as UNet to perform the area segmentation. Skip self-attention connection (SSA) block, comprising a shortcut and a parallel polarized self-attention (PPSA) block, has been proposed to deal with the challenges of blurred boundaries and irregular shapes of the retinal atrophic region. Further, we have proposed a multi-scale feature flow (MSFF) to challenge the size variation. We have added the flow between the SSA connection blocks, allowing for capturing considerable semantic information to detect retinal atrophy in various area sizes.
UNASSIGNED: The proposed method has been validated on the Pathological Myopia (PALM) dataset. Experimental results demonstrate that our method yields a high dice coefficient (DICE) of 84.26%, Jaccard index (JAC) of 72.80%, and F1-score of 84.57%, which outperforms other methods significantly.
UNASSIGNED: Our results have demonstrated that ARA-Net is an effective and efficient approach for retinal atrophic area segmentation in PM.
摘要：
■准确检测和分割视网膜萎缩区域对于病理性近视（PM）的早期医学干预至关重要。然而,基于二维(2D)眼底图像分割视网膜萎缩区域提出了几个挑战，比如模糊的边界，不规则形状,和大小变化。为了克服这些挑战，我们提出了一种注意力感知的视网膜萎缩分割网络（ARA-Net），用于从2D眼底图像中分割视网膜萎缩区域。
■特别是，ARA-Net采用与UNet类似的策略来执行区域分割。跳过自注意连接(SSA)块，包括快捷方式和平行极化自我注意(PPSA)块，已经提出了应对视网膜萎缩区域的边界模糊和形状不规则的挑战。Further,我们提出了一种多尺度特征流(MSFF)来挑战尺寸变化。我们在SSA连接块之间添加了流，允许捕获大量的语义信息来检测各种区域大小的视网膜萎缩。
■所提出的方法已在病理性近视（PALM）数据集上进行了验证。实验结果表明，我们的方法产生84.26％的高骰子系数（DICE），Jaccard指数（JAC）为72.80%，F1得分为84.57％，它的性能明显优于其他方法。
■我们的结果表明，ARA-Net是PM中视网膜萎缩性区域分割的一种有效且高效的方法。
■特别是，ARA-Net采用与UNet类似的策略来执行区域分割。跳过自注意连接(SSA)块，包括快捷方式和平行极化自我注意(PPSA)块，已经提出了应对视网膜萎缩区域的边界模糊和形状不规则的挑战。Further,我们提出了一种多尺度特征流(MSFF)来挑战尺寸变化。我们在SSA连接块之间添加了流，允许捕获大量的语义信息来检测各种区域大小的视网膜萎缩。
■所提出的方法已在病理性近视（PALM）数据集上进行了验证。实验结果表明，我们的方法产生84.26％的高骰子系数（DICE），Jaccard指数（JAC）为72.80%，F1得分为84.57％，它的性能明显优于其他方法。
■我们的结果表明，ARA-Net是PM中视网膜萎缩性区域分割的一种有效且高效的方法。
