| 冯长锋,劳群,丁忠祥,王罗羽,王天宇,郗玉珍,韩静,何林阳,沈起钧.基于特征金字塔网络自动分割平扫CT所示自发性脑出血血肿并判断其语义特征[J].中国医学影像技术,2024,40(10):1487~1492 |
| 基于特征金字塔网络自动分割平扫CT所示自发性脑出血血肿并判断其语义特征 |
| Feature pyramid network for automatic segmentation and semantic feature classification of spontaneous intracerebral hemorrhage hematoma on non-contrast CT images |
| 投稿时间：2024-02-15 修订日期：2024-05-30 |
| DOI：10.13929/j.issn.1003-3289.2024.10.007 |
| 中文关键词: 脑出血 血肿 体层摄影术,X线计算机 深度学习 |
| 英文关键词:cerebral hemorrhage hematoma tomography,X-ray computed deep learning |
| 基金项目: |
| 作者 | 单位 | E-mail | | 冯长锋 | 杭州市儿童医院放射科, 浙江 杭州 310005
浙江大学医学院附属杭州市第一人民医院放射科, 浙江 杭州 310003 | | | 劳群 | 杭州市儿童医院放射科, 浙江 杭州 310005 | | | 丁忠祥 | 浙江大学医学院附属杭州市第一人民医院放射科, 浙江 杭州 310003 | | | 王罗羽 | 浙江大学医学院附属杭州市第一人民医院放射科, 浙江 杭州 310003 | | | 王天宇 | 浙江大学医学院附属杭州市第一人民医院放射科, 浙江 杭州 310003 | | | 郗玉珍 | 中国人民解放军联勤保障部队第903医院放射科, 浙江 杭州 310012 | | | 韩静 | 浙江康静医院放射科, 浙江 杭州 310064 | | | 何林阳 | 杭州健培科技有限公司, 浙江 杭州 311200 | | | 沈起钧 | 浙江大学医学院附属杭州市第一人民医院放射科, 浙江 杭州 310003 | shenqijun80@163.com |
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| 中文摘要: |
| 目的 观察基于特征金字塔网络(FPN)自动分割平扫CT所示自发性脑出血(sICH)血肿并判断其语义特征的价值。方法 回顾性收集A医院408例(训练集)及B医院103例(验证集)sICH平扫CT图像;基于FPN构建深度学习(DL)分割模型分割血肿区域,并以交并比(IoU)、戴斯相似系数(DSC)及准确率评价其效能;以DL分类模型判断血肿语义特征,绘制受试者工作特征曲线,计算曲线下面积(AUC),评估其识别sICH血肿语义特征的效能。结果 DL分割模型分割训练集95% sICH血肿的IoU、DSC及准确率分别为0.84±0.07、0.91±0.04及(88.78±8.04)%,在验证集分别为0.83±0.07、0.91±0.05及(88.59±7.76)%。DL分类模型识别训练集及验证集sICH血肿不规则形态、不均匀密度、卫星征、混杂征及漩涡征的AUC分别为0.946~0.993及0.714~0.833。结论 基于FPN可准确、高效地自动分割sICH血肿,对于判断血肿语义特征亦具有较高效能。 |
| 英文摘要: |
| Objective To observe the value of feature pyramid network (FPN) for automatic segmentation and semantic feature classification of spontaneous intracerebral hemorrhage (sICH) hematoma showed on non-contrast CT. Methods Non-contrast CT images of 408 sICH patients in hospital A (training set) and 103 sICH patients in hospital B (validation set) were retrospectively analyzed. Deep learning (DL) segmentation model was constructed based on FPN to segment the hematoma region, and its efficacy was assessed using intersection over union (IoU), Dice similarity coefficient (DSC) and accuracy. Then DL classification model was established to identify the semantic features of sICH hematoma. Receiver operating characteristic curves were drawn, and the area under the curves (AUC) were calculated to evaluate the efficacy of DL classification model for recognizing semantic features of sICH hematoma. Results The IoU, DSC and accuracy of DL segmentation model for 95% sICH hematoma in training set was 0.84±0.07, 0.91±0.04 and (88.78±8.04)%, respectively, which was 0.83±0.07, 0.91±0.05 and (88.59±7.76)% in validation set, respectively. The AUC of DL classification model for recognizing irregular shape, uneven density, satellite sign, mixed sign and vortex sign of sICH hematoma were 0.946—0.993 and 0.714—0.833 in training set and validation set, respectively.Conclusions FPN could accurately, effectively and automatically segment hematoma of sICH, hence having high efficacy for identifying semantic features of sICH hematoma. |
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