Learning to segment fetal brain tissue from noisy annotations.
Learning to segment fetal brain tissue from noisy annotations.
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学习从嘈杂的注释中分割胎儿脑组织。
DOI：
10.1016/j.media.2022.102731
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发表时间：
2023
影响因子：
10.9
通讯作者：
Gholipour,Ali
中科院分区：
文献类型：
--
作者：
Karimi,Davood;Rollins,CaitlinK;Velasco-Annis,Clemente;Ouaalam,Abdelhakim;Gholipour,Ali
在这一关键阶段，自动分割胎儿脑组织可以增强对大脑发育的定量评估。深度学习方法代表了医学图像分割的最新水平，在脑分割中也取得了令人印象深刻的结果。然而，要有效地训练深度学习模型来执行这一任务，需要大量的训练图像来代表胎儿大脑结构的快速发展。另一方面，人工对大量3D图像进行多标签分割是令人望而却步的。为了应对这一挑战，我们使用基于变形配准和概率图谱融合的自动多图谱分割策略分割了272张训练图像，覆盖19-39周，并手动纠正了这些分割中的大错误。由于这个过程产生了一个带有噪声分割的大型训练数据集，我们提出了一种新的标签平滑过程和一个损失函数来训练带有平滑噪声分割的深度学习模型。我们提出的方法很好地考虑了组织边界的不确定性。我们在一组单独的胎儿的23张手工分割的测试图像上对我们的方法进行了评估。结果表明，对于较年轻和较大胎儿的暂态结构，我们的方法的平均Dice相似系数分别为0.893和0.916。我们的方法产生的结果比包括NNU-Net在内的几种最先进的方法产生的结果要准确得多，NNU-Net的结果与我们的方法最接近。我们训练的模型可以作为一个有价值的工具来提高磁共振胎儿脑分析的准确性和重复性。
Automatic fetal brain tissue segmentation can enhance the quantitative assessment of brain development at this critical stage. Deep learning methods represent the state of the art in medical image segmentation and have also achieved impressive results in brain segmentation. However, effective training of a deep learning model to perform this task requires a large number of training images to represent the rapid development of the transient fetal brain structures. On the other hand, manual multi-label segmentation of a large number of 3D images is prohibitive. To address this challenge, we segmented 272 training images, covering 19–39 gestational weeks, using an automatic multi-atlas segmentation strategy based on deformable registration and probabilistic atlas fusion, and manually corrected large errors in those segmentations. Since this process generated a large training dataset with noisy segmentations, we developed a novel label smoothing procedure and a loss function to train a deep learning model with smoothed noisy segmentations. Our proposed methods properly account for the uncertainty in tissue boundaries. We evaluated our method on 23 manually-segmented test images of a separate set of fetuses. Results show that our method achieves an average Dice similarity coefficient of 0.893 and 0.916 for the transient structures of younger and older fetuses, respectively. Our method generated results that were significantly more accurate than several state-of-the-art methods including nnU-Net that achieved the closest results to our method. Our trained model can serve as a valuable tool to enhance the accuracy and reproducibility of fetal brain analysis in MRI.