Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (12): 3918-3926.DOI: 10.11772/j.issn.1001-9081.2023010045
• Multimedia computing and computer simulation • Previous Articles Next Articles
Donglei LAN1,2(), Xiaodong WANG1,2, Yu YAO1,2, Xin WANG3, Jitao ZHOU3
Received:
2023-01-17
Revised:
2023-03-15
Accepted:
2023-03-16
Online:
2023-06-06
Published:
2023-12-10
Contact:
Donglei LAN
About author:
WANG Xiaodong, born in 1973, research fellow. His research interests include network engineering.Supported by:
兰冬雷1,2(), 王晓东1,2, 姚宇1,2, 王辛3, 周继陶3
通讯作者:
兰冬雷
作者简介:
王晓东(1973—),男,四川乐山人,研究员,主要研究方向:网络工程基金资助:
CLC Number:
Donglei LAN, Xiaodong WANG, Yu YAO, Xin WANG, Jitao ZHOU. Rectal cancer segmentation network based on adjacent slice attention fusion[J]. Journal of Computer Applications, 2023, 43(12): 3918-3926.
兰冬雷, 王晓东, 姚宇, 王辛, 周继陶. 基于邻近切片注意力融合的直肠癌分割网络[J]. 《计算机应用》唯一官方网站, 2023, 43(12): 3918-3926.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023010045
网络 | backbone | 参数量/106 | 浮点运算量/GFLOPs | mean IoU/% | mean DSC/% | mean 95% HD/mm |
---|---|---|---|---|---|---|
FCN | ResNet18 | 16.65 | 35.00 | 56.84±2.78 | 70.83±2.70 | 32.45±10.21 |
U-Net | U-Net | 33.77 | 49.45 | 59.92±3.03 | 73.15±2.83 | 29.20±6.18 |
DeepLabV3 | U-Net | 38.63 | 67.96 | 60.53±3.62 | 73.93±2.90 | 28.57±9.23 |
HRNetV2 | HRNet | 16.78 | 27.57 | 61.39±3.03 | 74.70±2.83 | 26.37±6.44 |
本文网络 | HRNet | 23.73 | 34.36 | 63.07±3.30 | 75.96±2.86 | 25.46±5.62 |
Tab.1 Comparison of segmentation results of different networks
网络 | backbone | 参数量/106 | 浮点运算量/GFLOPs | mean IoU/% | mean DSC/% | mean 95% HD/mm |
---|---|---|---|---|---|---|
FCN | ResNet18 | 16.65 | 35.00 | 56.84±2.78 | 70.83±2.70 | 32.45±10.21 |
U-Net | U-Net | 33.77 | 49.45 | 59.92±3.03 | 73.15±2.83 | 29.20±6.18 |
DeepLabV3 | U-Net | 38.63 | 67.96 | 60.53±3.62 | 73.93±2.90 | 28.57±9.23 |
HRNetV2 | HRNet | 16.78 | 27.57 | 61.39±3.03 | 74.70±2.83 | 26.37±6.44 |
本文网络 | HRNet | 23.73 | 34.36 | 63.07±3.30 | 75.96±2.86 | 25.46±5.62 |
网络 | IoU/% | DSC/% | 95% HD/mm | ||||||
---|---|---|---|---|---|---|---|---|---|
肿瘤 | 直肠系膜 | 外淋巴结区 | 肿瘤 | 直肠系膜 | 外淋巴结区 | 肿瘤 | 直肠系膜 | 外淋巴结区 | |
FCN | 33.95±5.56 | 71.36±4.68 | 65.21±3.79 | 50.42±6.49 | 83.20±3.15 | 78.88±2.83 | 43.65±10.23 | 25.38±5.25 | 29.24±8.67 |
U-Net | 34.94±5.32 | 73.35±3.39 | 71.48±3.21 | 51.54±6.18 | 84.58±2.23 | 83.33±2.16 | 41.26±12.67 | 23.51±3.77 | 22.84±6.36 |
DeepLabV3 | 37.50±4.15 | 72.89±4.82 | 71.21±3.37 | 54.42±4.32 | 84.23±3.28 | 83.13±2.35 | 39.31±15.18 | 22.31±7.62 | 24.11±6.07 |
HRNetV2 | 39.85±5.32 | 77.17±3.39 | 67.13±3.21 | 56.87±6.18 | 87.03±2.23 | 80.18±2.16 | 38.07±11.36 | 16.80±3.79 | 24.24±5.64 |
本文网络 | 40.73±6.28 | 76.71±2.65 | 71.78±2.15 | 57.61±6.25 | 86.73±1.67 | 83.56±1.44 | 38.04±11.46 | 17.60±3.18 | 20.75±3.76 |
Tab.2 Comparison of multi-object segmentation results of different networks
网络 | IoU/% | DSC/% | 95% HD/mm | ||||||
---|---|---|---|---|---|---|---|---|---|
肿瘤 | 直肠系膜 | 外淋巴结区 | 肿瘤 | 直肠系膜 | 外淋巴结区 | 肿瘤 | 直肠系膜 | 外淋巴结区 | |
FCN | 33.95±5.56 | 71.36±4.68 | 65.21±3.79 | 50.42±6.49 | 83.20±3.15 | 78.88±2.83 | 43.65±10.23 | 25.38±5.25 | 29.24±8.67 |
U-Net | 34.94±5.32 | 73.35±3.39 | 71.48±3.21 | 51.54±6.18 | 84.58±2.23 | 83.33±2.16 | 41.26±12.67 | 23.51±3.77 | 22.84±6.36 |
DeepLabV3 | 37.50±4.15 | 72.89±4.82 | 71.21±3.37 | 54.42±4.32 | 84.23±3.28 | 83.13±2.35 | 39.31±15.18 | 22.31±7.62 | 24.11±6.07 |
HRNetV2 | 39.85±5.32 | 77.17±3.39 | 67.13±3.21 | 56.87±6.18 | 87.03±2.23 | 80.18±2.16 | 38.07±11.36 | 16.80±3.79 | 24.24±5.64 |
本文网络 | 40.73±6.28 | 76.71±2.65 | 71.78±2.15 | 57.61±6.25 | 86.73±1.67 | 83.56±1.44 | 38.04±11.46 | 17.60±3.18 | 20.75±3.76 |
方法 | backbone | 分割头 | 消融模块 | 平均IoU/% | 平均DSC/% | 平均95%HD/mm | |
---|---|---|---|---|---|---|---|
ASAF模块 | 一致性损失 | ||||||
① | HRNet | FCN+ASPP | 61.83±2.73 | 74.89±3.17 | 26.42±7.45 | ||
② | HRNet | FCN+ASPP | √ | 62.94±4.32 | 75.47±2.54 | 25.87±6.42 | |
③ | HRNet | FCN+ASPP | √ | 62.11±3.22 | 75.05±3.03 | 26.25±6.88 | |
④ | HRNet | FCN+ASPP | √ | √ | 63.07±3.30 | 75.96±2.86 | 25.46±5.62 |
Tab. 3 Comparison of network structure ablation experimental results
方法 | backbone | 分割头 | 消融模块 | 平均IoU/% | 平均DSC/% | 平均95%HD/mm | |
---|---|---|---|---|---|---|---|
ASAF模块 | 一致性损失 | ||||||
① | HRNet | FCN+ASPP | 61.83±2.73 | 74.89±3.17 | 26.42±7.45 | ||
② | HRNet | FCN+ASPP | √ | 62.94±4.32 | 75.47±2.54 | 25.87±6.42 | |
③ | HRNet | FCN+ASPP | √ | 62.11±3.22 | 75.05±3.03 | 26.25±6.88 | |
④ | HRNet | FCN+ASPP | √ | √ | 63.07±3.30 | 75.96±2.86 | 25.46±5.62 |
方法 | backbone | neck | 分割头 | 参数量/106 | 浮点运算量 /GFLOPs | 评价指标 | ||
---|---|---|---|---|---|---|---|---|
平均IoU/% | 平均DSC/% | 平均95%HD/mm | ||||||
⑤ | ResNet | — | FCN | 16.65 | 35.00 | 56.84±2.78 | 70.83±2.70 | 32.45±10.21 |
⑥ | ResNet | ASAF模块 | FCN | 23.63 | 48.76 | 56.96±5.46 | 71.05±4.34 | 30.74±9.34 |
⑦ | U-Net | — | FCN | 33.77 | 49.45 | 59.92±3.03 | 73.15±2.83 | 29.20±6.18 |
⑧ | U-Net | ASAF模块 | FCN | 38.84 | 85.69 | 60.73±4.32 | 74.07±6.23 | 28.46±9.73 |
Tab.4 Ablation experimental results of ASAF module generalization ability
方法 | backbone | neck | 分割头 | 参数量/106 | 浮点运算量 /GFLOPs | 评价指标 | ||
---|---|---|---|---|---|---|---|---|
平均IoU/% | 平均DSC/% | 平均95%HD/mm | ||||||
⑤ | ResNet | — | FCN | 16.65 | 35.00 | 56.84±2.78 | 70.83±2.70 | 32.45±10.21 |
⑥ | ResNet | ASAF模块 | FCN | 23.63 | 48.76 | 56.96±5.46 | 71.05±4.34 | 30.74±9.34 |
⑦ | U-Net | — | FCN | 33.77 | 49.45 | 59.92±3.03 | 73.15±2.83 | 29.20±6.18 |
⑧ | U-Net | ASAF模块 | FCN | 38.84 | 85.69 | 60.73±4.32 | 74.07±6.23 | 28.46±9.73 |
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