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首页> 外文期刊>Journal of healthcare engineering. >Automated Segmentation of Colorectal Tumor in 3D MRI Using 3D Multiscale Densely Connected Convolutional Neural Network
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Automated Segmentation of Colorectal Tumor in 3D MRI Using 3D Multiscale Densely Connected Convolutional Neural Network

机译：3D MRI在3D MERI中的自动分割使用3D多尺度密集连接卷积神经网络

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The main goal of this work is to automatically segment colorectal tumors in 3D T2-weighted (T2w) MRI with reasonable accuracy. For such a purpose, a novel deep learning-based algorithm suited for volumetric colorectal tumor segmentation is proposed. The proposed CNN architecture, based on densely connected neural network, contains multiscale dense interconnectivity between layers of fine and coarse scales, thus leveraging multiscale contextual information in the network to get better flow of information throughout the network. Additionally, the 3D level-set algorithm was incorporated as a postprocessing task to refine contours of the network predicted segmentation. The method was assessed on T2-weighted 3D MRI of 43 patients diagnosed with locally advanced colorectal tumor (cT3/T4). Cross validation was performed in 100 rounds by partitioning the dataset into 30 volumes for training and 13 for testing. Three performance metrics were computed to assess the similarity between predicted segmentation and the ground truth (i.e., manual segmentation by an expert radiologist/oncologist), including Dice similarity coefficient (DSC), recall rate (RR), and average surface distance (ASD). The above performance metrics were computed in terms of mean and standard deviation (mean ± standard deviation). The DSC, RR, and ASD were 0.8406 ±0.0191, 0.8513 ±0.0201, and 2.6407 ± 2.7975 before postprocessing, and these performance metrics became 0.8585 ± 0.0184,0.8719 ± 0.0195, and 2.5401 ± 2.402 after postprocessing, respectively. We compared our proposed method to other existing volumetric medical image segmentation baseline methods (particularly 3D U-net and DenseVoxNet) in our segmentation tasks. The experimental results reveal that the proposed method has achieved better performance in colorectal tumor segmentation in volumetric MRI than the other baseline techniques.
机译：这项工作的主要目的是在3D T2加权（T2W）MRI中自动分段结直肠肿瘤，具有合理的准确性。为此目的，提出了一种适用于体积结直肠肿瘤分割的新型基于深度学习的算法。基于密集连接的神经网络的建议的CNN架构包含多尺寸与粗略尺度层之间的多尺度密集互连，从而利用网络中的多尺度上下文信息来在整个网络中获得更好的信息流。另外，3D级别集算法被用作后处理任务以改进网络预测分割的轮廓。在诊断患有局部晚期结直肠肿瘤（CT3 / T4）的43名患者的T2加权3D MRI上评估该方法。通过将数据集分区为30卷以进行培训和13以进行测试，在100轮进行交叉验证。计算了三个性能指标以评估预测分割和地面真理之间的相似性（即，专家放射科医生/肿瘤科学家的手动分段），包括骰子相似度系数（DSC），召回率（RR）和平均表面距离（ASD） 。以平均值和标准偏差（平均值±标准偏差）计算上述性能度量。在后处理之前，DSC，RR和ASD为0.8406±0.0191,0.8513±0.01,20191,0.8513±0.01和2.6407±2.7975，后处理后，这些性能度量分别变为0.8585±0.0184,0.8719±0.0195和2.5401±2.402。我们将所提出的方法与其他现有的体积医学图像分割基线方法（特别是3D U-Net和DendvoxNet）进行比较。实验结果表明，该方法在体积MRI中的结肠直肠肿瘤分段中取得了更好的性能，而不是其他基线技术。

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