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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >A Pipeline for Lung Tumor Detection and Segmentation from CT Scans Using Dilated Convolutional Neural Networks
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A Pipeline for Lung Tumor Detection and Segmentation from CT Scans Using Dilated Convolutional Neural Networks

机译：使用膨胀卷积神经网络从CT扫描中检测和分割肺肿瘤的管道

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Lung cancer is the most prevalent cancer worldwide with about 230,000 new cases every year. Most cases go undiagnosed until it's too late, especially in developing countries and remote areas. Early detection is key to beating cancer. Towards this end, the work presented here proposes an automated pipeline for lung tumor detection and segmentation from 3D lung CT scans from the NSCLC-Radiomics Dataset. It also presents a new dilated hybrid-3D convolutional neural network architecture for tumor segmentation. First, a binary classifier chooses CT scan slices that may contain parts of a tumor. To segment the tumors, the selected slices are passed to the segmentation model which extracts feature maps from each 2D slice using dilated convolutions and then fuses the stacked maps through 3D convolutions - incorporating the 3D structural information present in the CT scan volume into the output. Lastly, the segmentation masks are passed through a post-processing block which cleans them up through morphological operations. The proposed segmentation model outperformed other contemporary models like LungNet and U-Net. The average and median dice coefficient on the test set for the proposed model were 65.7% and 70.39% respectively. The next best model, LungNet had dice scores of 62.67% and 66.78%.
机译：肺癌是全球最流行的癌症，每年约有23万例新病例。直到为时已晚，大多数情况才被诊断出来，尤其是在发展中国家和偏远地区。早期发现是战胜癌症的关键。为此，本文提出的工作为从NSCLC-Radiomics数据集进行的3D肺部CT扫描提出了用于肺部肿瘤检测和分割的自动化管道。它还提出了一种用于肿瘤分割的新的扩张式杂交3D卷积神经网络架构。首先，二元分类器选择可能包含肿瘤部分的CT扫描切片。为了分割肿瘤，将选定的切片传递到分割模型，该模型使用膨胀卷积从每个2D切片中提取特征图，然后通过3D卷积融合堆叠的图-将CT扫描量中存在的3D结构信息合并到输出中。最后，分割蒙版通过后处理模块，该模块通过形态学操作将其清理干净。提议的细分模型优于其他当代模型，例如LungNet和U-Net。该模型在测试集上的平均骰子系数和中值骰子系数分别为65.7％和70.39％。次之，LungNet的骰子得分分别为62.67％和66.78％。

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