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2021, Human Brain Mapping. DOI: 10.1002/hbm.25739 PMID: 34873789 PMCID: PMC8886667
Performance of three freely available methods for extracting white matter hyperintensities: FreeSurfer, UBO Detector, and BIANCA
Isabel Hotz, Pascal Frédéric Deschwanden, Franziskus Liem, Susan Mérillat, Brigitta Malagurski, Spyros Kollias, Lutz Jäncke
Abstract:
White matter hyperintensities (WMH) of presumed vascular origin are frequently found in MRIs of healthy older adults. WMH are also associated with aging and cognitive decline. Here, we compared and validated three algorithms for WMH extraction: FreeSurfer (T1w), UBO Detector (T1w + FLAIR), and FSL's Brain Intensity AbNormality Classification Algorithm (BIANCA; T1w + FLAIR) using a longitudinal dataset comprising MRI data of cognitively healthy older adults (baseline N = 231, age range 64-87 years). As reference we manually segmented WMH in T1w, three-dimensional (3D) FLAIR, and two-dimensional (2D) FLAIR images which were used to assess the segmentation accuracy of the different automated algorithms. Further, we assessed the relationships of WMH volumes provided by the algorithms with Fazekas scores and age. FreeSurfer underestimated the WMH volumes and scored worst in Dice Similarity Coefficient (DSC = 0.434) but its WMH volumes strongly correlated with the Fazekas scores (rs = 0.73). BIANCA accomplished the highest DSC (0.602) in 3D FLAIR images. However, the relations with the Fazekas scores were only moderate, especially in the 2D FLAIR images (rs = 0.41), and many outlier WMH volumes were detected when exploring within-person trajectories (2D FLAIR: ~30%). UBO Detector performed similarly to BIANCA in DSC with both modalities and reached the best DSC in 2D FLAIR (0.531) without requiring a tailored training dataset. In addition, it achieved very high associations with the Fazekas scores (2D FLAIR: rs = 0.80). In summary, our results emphasize the importance of carefully contemplating the choice of the WMH segmentation algorithm and MR-modality.
2022-04-30 14:19:00
#paper https://doi.org/10.1002/hbm.25739 推测为血管源性的脑白质高信号(WMH)常在健康老年人群的MRI上有发现。WMH还与衰老和认知能力下降有关。本文使用包含认知健康老年人MRI数据的纵向数据集(基线N=231人,年龄范围在64~87岁之间),比较并验证了FreeSurfer (T1w)、UBO Detector (T1W + FLAIR)和FSL-BIANCA(T1w+FLAIR)三种脑白质高信号提取的算法的有效性。作为参考,我们在T1w、3D (3D) FLAIR和二维(2D) FLAIR图像中手动分割WMH,并用于评估不同自动化算法的分割精度。此外,我们评估了算法提供的WMH体积与Fazekas评分和年龄的关系。FreeSurfer低估了WMH的体积,其骰子相似系数最差(DSC = 0.434),但其WMH的体积与Fazekas得分有很强的相关性(rs = 0.73)。BIANCA在3D FLAIR图像中实现了最高DSC(0.602)。然而,在2D FLAIR图像中(rs = 0.41),与Fazekas得分的关系仅为中等,在探索人体内轨迹时检测到许多异常值WMH体积(2D FLAIR: ~30%)。UBO Detector在DSC中与BIANCA在两种模式下的表现相似,在2D FLAIR(0.531)中达到了最佳DSC,无需定制训练数据集。此外,它与Fazekas评分有很高的相关性(2D FLAIR: rs = 0.80)。总之,我们的结果强调了仔细考虑选择的WMH分割算法和mr模态的重要性。
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