论文标题
RATLESNETV2：用于啮齿动物脑病变分段的完全卷积网络
RatLesNetv2: A Fully Convolutional Network for Rodent Brain Lesion Segmentation
论文作者
论文摘要
我们提出了一个完全卷积神经网络（Convnet），称为RatlesNetv2，用于分割啮齿动物磁共振（MR）脑图像中的病变。 RATLESNETV2架构类似于自动编码器，并结合了有助于其优化的残留块。 RatlesNETV2在三维图像上受过训练，不需要预处理。我们在一个非常大的数据集上评估了RATLESNETV2，该数据集由916个T2加权大鼠脑MRI扫描组成，该扫描在9个不同的病变阶段，用于研究药物开发的局灶性脑缺血。此外，我们将其性能与专门为医疗图像分割设计的其他三个Convnet进行了比较。 RatlesNETV2与其他Convnet相比，获得的骰子系数值类似于更高的骰子系数，并且它产生了更现实，更紧凑的分割，孔明显更少，而Hausdorff距离较低。 RATLESNETV2分割的骰子得分也超过了手动分割的评估者间一致性。总之，RATLESNETV2可用于自动病变分割，减少人类工作量并提高可重复性。 RATLESNETV2可在https://github.com/jmlipman/ratlesnetv2上公开获得。
We present a fully convolutional neural network (ConvNet), named RatLesNetv2, for segmenting lesions in rodent magnetic resonance (MR) brain images. RatLesNetv2 architecture resembles an autoencoder and it incorporates residual blocks that facilitate its optimization. RatLesNetv2 is trained end to end on three-dimensional images and it requires no preprocessing. We evaluated RatLesNetv2 on an exceptionally large dataset composed of 916 T2-weighted rat brain MRI scans of 671 rats at nine different lesion stages that were used to study focal cerebral ischemia for drug development. In addition, we compared its performance with three other ConvNets specifically designed for medical image segmentation. RatLesNetv2 obtained similar to higher Dice coefficient values than the other ConvNets and it produced much more realistic and compact segmentations with notably fewer holes and lower Hausdorff distance. The Dice scores of RatLesNetv2 segmentations also exceeded inter-rater agreement of manual segmentations. In conclusion, RatLesNetv2 could be used for automated lesion segmentation, reducing human workload and improving reproducibility. RatLesNetv2 is publicly available at https://github.com/jmlipman/RatLesNetv2.
