School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China.
Sensors (Basel). 2024 Jan 31;24(3):928. doi: 10.3390/s24030928.
Fluorescence in situ hybridization (FISH) is a powerful cytogenetic method used to precisely detect and localize nucleic acid sequences. This technique is proving to be an invaluable tool in medical diagnostics and has made significant contributions to biology and the life sciences. However, the number of cells is large and the nucleic acid sequences are disorganized in the FISH images taken using the microscope. Processing and analyzing images is a time-consuming and laborious task for researchers, as it can easily tire the human eyes and lead to errors in judgment. In recent years, deep learning has made significant progress in the field of medical imaging, especially the successful application of introducing the attention mechanism. The attention mechanism, as a key component of deep learning, improves the understanding and interpretation of medical images by giving different weights to different regions of the image, enabling the model to focus more on important features. To address the challenges in FISH image analysis, we combined medical imaging with deep learning to develop the SEAM-Unet++ automated cell contour segmentation algorithm with integrated attention mechanism. The significant advantage of this algorithm is that it improves the accuracy of cell contours in FISH images. Experiments have demonstrated that by introducing the attention mechanism, our method is able to segment cells that are adherent to each other more efficiently.
荧光原位杂交（FISH）是一种强大的细胞遗传学方法，用于精确检测和定位核酸序列。这项技术在医学诊断中被证明是一种非常有价值的工具，并为生物学和生命科学做出了重大贡献。然而，在使用显微镜拍摄的 FISH 图像中，细胞数量众多，核酸序列混乱。对于研究人员来说，处理和分析图像是一项耗时费力的任务，因为它容易使眼睛疲劳，并导致判断错误。近年来，深度学习在医学成像领域取得了重大进展，特别是引入注意力机制的成功应用。注意力机制作为深度学习的关键组成部分，通过为图像的不同区域赋予不同的权重，提高了对医学图像的理解和解释能力，使模型能够更专注于重要特征。为了解决 FISH 图像分析中的挑战，我们将医学成像与深度学习相结合，开发了具有集成注意力机制的 SEAM-Unet++ 自动细胞轮廓分割算法。该算法的显著优势在于提高了 FISH 图像中细胞轮廓的准确性。实验表明，通过引入注意力机制，我们的方法能够更有效地分割相互粘连的细胞。
