LMBiS-Net：一种基于轻量级多路径双向跳跃连接的CNN用于视网膜血管分割
LMBiS-Net: A Lightweight Multipath Bidirectional Skip Connection based CNN for Retinal Blood Vessel Segmentation
AI总结：
针对视网膜血管分割中边缘信息丢失和参数量大的问题，提出轻量级CNN模型LMBiS-Net，通过多路径特征提取和双向跳跃连接提升分割精度，仅含0.172M参数，兼顾了计算效率与临床准确性。
摘要：
致盲性眼病通常与视网膜形态改变相关，这可以通过在眼底图像中分割视网膜结构来进行临床识别。然而，当前的方法在准确分割细微血管方面往往存在不足。尽管深度学习在医学图像分割中展现出潜力，但其对重复卷积和池化操作的依赖会阻碍边缘信息的表示，最终限制整体分割准确率。在本文中，我们提出了一种名为LMBiS-Net的轻量级像素级CNN，用于视网膜血管分割，其可学习参数数量极少（仅0.172 M）。该网络使用多路径特征提取块，并结合双向跳跃连接来实现编码器与解码器之间的信息流。此外，我们通过仔细选择滤波器数量以避免滤波器重叠，从而优化了模型效率。这种优化显著减少了训练时间并提高了计算效率。为了评估LMBiS-Net的鲁棒性和泛化能力，我们在视网膜图像的各个方面进行了全面评估。具体而言，该模型经过了严格的测试以准确分割视网膜血管，这在眼科诊断和治疗中起着至关重要的作用。通过聚焦视网膜血管，我们能够全面分析LMBiS-Net模型的性能和有效性。我们的测试结果表明，LMBiS-Net不仅具有鲁棒性和泛化能力，而且能够保持高水平的分割准确率。这些特点凸显了LMBiS-Net作为一种高效工具的潜力，可在各种临床应用中实现高速且准确的视网膜图像分割。
英文摘要：
Blinding eye diseases are often correlated with altered retinal morphology, which can be clinically identified by segmenting retinal structures in fundus images. However, current methodologies often fall short in accurately segmenting delicate vessels. Although deep learning has shown promise in medical image segmentation, its reliance on repeated convolution and pooling operations can hinder the representation of edge information, ultimately limiting overall segmentation accuracy. In this paper, we propose a lightweight pixel-level CNN named LMBiS-Net for the segmentation of retinal vessels with an exceptionally low number of learnable parameters \textbf{(only 0.172 M)}. The network used multipath feature extraction blocks and incorporates bidirectional skip connections for the information flow between the encoder and decoder. Additionally, we have optimized the efficiency of the model by carefully selecting the number of filters to avoid filter overlap. This optimization significantly reduces training time and enhances computational efficiency. To assess the robustness and generalizability of LMBiS-Net, we performed comprehensive evaluations on various aspects of retinal images. Specifically, the model was subjected to rigorous tests to accurately segment retinal vessels, which play a vital role in ophthalmological diagnosis and treatment. By focusing on the retinal blood vessels, we were able to thoroughly analyze the performance and effectiveness of the LMBiS-Net model. The results of our tests demonstrate that LMBiS-Net is not only robust and generalizable but also capable of maintaining high levels of segmentation accuracy. These characteristics highlight the potential of LMBiS-Net as an efficient tool for high-speed and accurate segmentation of retinal images in various clinical applications.
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