基于3D scSE-UNet的肝脏CT图像半监督学习分割方法

刘清清,周志勇,范国华,钱旭升,胡冀苏,陈光强,戴亚康

Semi-supervised learning segmentation method of liver CT images based on 3D scSE-UNet

Qing-qing LIU,Zhi-yong ZHOU,Guo-hua FAN,Xu-sheng QIAN,Ji-su HU,Guang-qiang CHEN,Ya-kang DAI

表 1 3D scSE-UNet网络结构表

Tab.1 Architecture of proposed 3D scSE-UNet

网络结构层 1） 特征图大小 卷积核参数 网络结构层 特征图大小 卷积核参数 1）注：第2列表示当前层的输出特征的大小及通道数； 第3列中[ ]表示卷积操作，“3×3×3，8”表示经过卷积核大小为3×3×3和8通道的卷积层； “Dropout_1+UpSampling3D_1”中“+”表示Concatenate_1是将Dropout_1与UpSampling3D_1跳跃连接. input 128×128×64×1 − scSE_block_1 16×16×8×128 − Conv3D_1 $ \begin{array}{*{20}{c}} {128 \times 128 \times 64 \times 8} \\ {128 \times 128 \times 64 \times 16} \end{array} $ $\left[{\begin{array}{*{20}{c} }{3 \times 3 \times 3,\;8}\\{3 \times 3 \times 3,\;16}\end{array} } \right]$ UpSampling3D_2 32×32×16×128 2×2×2 MaxPooling3D_1 64×64×32×16 2×2×2 Concatenate_2 32×32×16×192 Conv3D_3+UpSampling3D_2 Conv3D_2 $ \begin{array}{*{20}{c}} {64 \times 64 \times 32 \times 16} \\ {64 \times 64 \times 32 \times 32} \end{array} $ $\left[ {\begin{array}{*{20}{c} } {3 \times 3 \times 3,\;16} \\ {3 \times 3 \times 3,\;32} \end{array} } \right]$ Conv3D_7 $ \begin{array}{*{20}{c}} {32 \times 32 \times 16 \times 64} \\ {32 \times 32 \times 16 \times 64} \end{array} $ $\left[ {\begin{array}{*{20}{c} } {3 \times 3 \times 3,\;64} \\ {3 \times 3 \times 3,\;64} \end{array} } \right]$ MaxPooling3D_2 32×32×16×32 2×2×2 scSE_block_2 32×32×16×64 − Conv3D_3 $ \begin{array}{*{20}{c}} {32 \times 32 \times 16 \times 32} \\ {32 \times 32 \times 16 \times 64} \end{array} $ $\left[{\begin{array}{*{20}{c} } {3 \times 3 \times 3,\;32} \\ {3 \times 3 \times 3,\;64} \end{array} }\right]$ UpSampling3D_3 64×64×32×64 2×2×2 MaxPooling3D_3 16×16×8×64 2×2×2 Concatenate_3 64×64×32×96 Conv3D_2+UpSampling3D_3 Conv3D_4 $ \begin{array}{*{20}{c}} {16 \times 16 \times 8 \times 64} \\ {16 \times 16 \times 8 \times 128} \end{array} $ $\left[{\begin{array}{*{20}{c} } {3 \times 3 \times 3,\;64} \\ {3 \times 3 \times 3,\;128} \end{array} }\right]$ Conv3D_8 64×64×32×32 $\left[ {\begin{array}{*{20}{c} } {3 \times 3 \times 3,\;32} \\ {3 \times 3 \times 3,\;32} \end{array} } \right]$ Dropout_1 16×16×8×128 0.5 scSE_block_3 64×64×32×32 − MaxPooling3D_4 8×8×4×128 2×2×2 UpSampling3D_4 128×128×64×32 2×2×2 Conv3D_5 $ \begin{array}{*{20}{c}} {8 \times 8 \times 4 \times 128} \\ {8 \times 8 \times 4 \times 256} \end{array} $ $\left[{\begin{array}{*{20}{c} } {3 \times 3 \times 3,\;128} \\ {3 \times 3 \times 3,\;256} \end{array} }\right]$ Concatenate_4 128×128×64×48 Conv3D_1+UpSampling3D_4 Dropout_2 8×8×4×256 0.5 Conv3D_9 128×128×64×16 $\left[ {\begin{array}{*{20}{c} } {3 \times 3 \times 3,\;16} \\ {3 \times 3 \times 3,\;16} \end{array} } \right]$ UpSampling3D_1 16×16×8×256 2×2×2 scSE_block_4 128×128×64×16 − Concatenate_1 16×16×8×384 Dropout_1+UpSampling3D_1 Conv3D_10 128×128×64×1 [1×1×1，1] Conv3D_6 $ \begin{array}{*{20}{c}} {16 \times 16 \times 8 \times 128} \\ {16 \times 16 \times 8 \times 128} \end{array} $ $\left[{\begin{array}{*{20}{c} } {3 \times 3 \times 3,\;128} \\ {3 \times 3 \times 3,\;128} \end{array} }\right]$ − − −