The quantification of new or enlarged lesions from follow-up MRI scans is an important surrogate of clinical disease activity in patients with multiple sclerosis (MS). Not only is manual segmentation time consuming, but inter-rater variability is high. Currently, only a few fully automated methods are available. We address this gap in the field by employing a 3D convolutional neural network (CNN) with encoder-decoder architecture for fully automatic longitudinal lesion segmentation.

Input data consist of two fluid attenuated inversion recovery (FLAIR) images (baseline and follow-up) per patient. Each image is entered into the encoder and the feature maps are concatenated and then fed into the decoder. The output is a 3D mask indicating new or enlarged lesions (compared to the baseline scan). The proposed method was trained on 1809 single point and 1444 longitudinal patient data sets and then validated on 185 independent longitudinal data sets from two different scanners. From the two validation data sets, manual segmentations were available from three experienced raters, respectively. The performance of the proposed method was compared to the open source Lesion Segmentation Toolbox (LST), which is a current state-of-art longitudinal lesion segmentation method.

The mean lesion-wise inter-rater sensitivity was 62%, while the mean inter-rater number of false positive (FP) findings was 0.41 lesions per case. The two validated algorithms showed a mean sensitivity of 60% (CNN), 46% (LST) and a mean FP of 0.48 (CNN), 1.86 (LST) per case. Sensitivity and number of FP were not significantly different (p < 0.05) between the CNN and manual raters.

New or enlarged lesions counted by the CNN algorithm appeared to be comparable with manual expert ratings. The proposed algorithm seems to outperform currently available approaches, particularly LST. The high inter-rater variability in case of manual segmentation indicates the complexity of identifying new or enlarged lesions. An automated CNN-based approach can quickly provide an independent and deterministic assessment of new or enlarged lesions from baseline to follow-up scans with acceptable reliability.

后续MRI扫描对新病变或扩大病变的量化是多发性硬化症（MS）患者临床疾病活动的重要替代指标。手动分割不仅耗时，而且评分者间的可变性也很高。当前，只有几种全自动方法可用。我们通过采用具有编码器-解码器体系结构的3D卷积神经网络（CNN）进行全自动纵向病变分割，解决了该领域的空白。

输入数据包括每位患者两张液体衰减反转恢复（FLAIR）图像（基线和随访）。每个图像都输入到编码器中，然后将特征图连接起来，然后馈入解码器中。输出是一个3D遮罩，指示新的或扩大的病变（与基线扫描相比）。该方法在1809个单点和1444个纵向患者数据集上进行了训练，然后在来自两个不同扫描仪的185个独立纵向数据集上进行了验证。从这两个验证数据集中，可以分别从三个有经验的评分者那里进行手动细分。将该方法的性能与开放源病变分割工具箱（LST）进行了比较，后者是当前最新的纵向病变分割方法。

每个病灶的平均病灶间病灶间敏感性为62％，而假阳性（FP）的平均病房间病灶数目为0.41个病灶。两种经过验证的算法显示出每例平均灵敏度为60％（CNN），46％（LST），平均FP为0.48（CNN），1.86（LST）。CNN和手动评估者之间的敏感性和FP数量没有显着差异（p <0.05）。

由CNN算法计算出的新病灶或扩大病灶似乎与人工专家评级相当。所提出的算法似乎优于当前可用的方法，尤其是LST。在手动分割的情况下，评分者之间的高度可变性表明了识别新病变或扩大病变的复杂性。基于CNN的自动化方法可以快速，可靠地对基线或后续扫描的新病变或扩大病变提供独立的确定性评估。