多编码器U-Net用于口腔鳞状细胞癌图像分割

A. Pennisi, D. Bloisi, D. Nardi, S. Varricchio, F. M. Donini
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引用次数: 1

摘要

全球每年约有17万人死于口腔肿瘤。在本文中,我们的重点是口腔鳞状细胞癌(OSCC),它占口腔所有恶性肿瘤的80 - 90%。提出了一种基于深度学习的像素级全幻灯片图像(WSI)样本分割方法。提出的方法是通过多编码器结构对众所周知的U-Net体系结构进行修改。特别是,我们的网络,称为多编码器U-Net,是一个多编码器单解码器网络,它将图像作为输入并分割成块。对于每个贴图,都有一个编码器负责在潜在空间中对其进行编码,然后一个卷积层负责将这些贴图合并成一个单层。解码器的每一层都将之前的上采样层作为输入,并将其与多个编码器的相应层合并而成的层连接起来。实验在公开可用的口腔癌注释(ORCA)数据集上进行,该数据集包含来自TCGA存储库的注释数据。使用三种不同的质量指标获得的定量实验结果证明了该方法的有效性,达到82%的像素精度,0.82的骰子相似分数和0.72的平均交集超过联合。
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Multi-encoder U-Net for Oral Squamous Cell Carcinoma Image Segmentation
Oral tumors are responsible for about 170,000 deaths every year in the World. In this paper, we focus on oral squamous cell carcinoma (OSCC), which represents up to 80–90 % of all malignant neoplasms of the oral cavity. We present a novel deep learning-based method for segmenting whole slide image (WSI) samples at the pixel level. The proposed method is a modification of the well-known U-Net architecture through a multi-encoder structure. In particular, our network, called Multi-encoder U-Net, is a multi-encoder single decoder network that takes as input an image and splits it in tiles. For each tile, there is an encoder responsible for encoding it in the latent space, then a convolutional layer is responsible for merging the tiles into a single layer. Each layer of the decoder takes as input the previous up-sampled layer and concatenate it with the layer made by merging the corresponding layers of the multiple encoders. Experiments have been carried out on the publicly available ORal Cancer Annotated (ORCA) dataset, which contains annotated data from the TCGA repository. Quantitative experimental results, obtained using three different quality metrics, demonstrate the effectiveness of the proposed approach, which achieves 82% Pixel-wise Accuracy, 0.82 Dice similarity score, and 0.72 Mean Intersection Over Union.
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