@article {aWang19, title = {Benchmark on Automatic 6-month-old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge}, journal = {IEEE Transactions on Medical Imaging}, year = {2019}, month = {2019/2/27}, abstract = {

Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging. Despite many efforts were devoted to brain segmentation, only few studies have focused on the segmentation of 6-month infant brain images. With the idea of boosting methodological development in the community, iSeg-2017 challenge (http://iseg2017.web.unc.edu) provides a set of 6-month infant subjects with manual labels for training and testing the participating methods. Among the 21 automatic segmentation methods participating in iSeg-2017, we review the 8 top-ranked teams, in terms of Dice ratio, modified Hausdorff distance and average surface distance, and introduce their pipelines, implementations, as well as source codes. We further discuss limitations and possible future directions. We hope the dataset in iSeg-2017 and this review article could provide insights into methodological development for the community.

}, doi = {10.1109/TMI.2019.2901712}, author = {Li Wang and Dong Nie and Guannan Li and Elodie Puybareau and Jose Dolz and Qian Zhang and Fan Wang and Jing Xia and Zhengwang Wu and Jiawei Chen and Kim-HanThung and Toan Duc Bui and Jitae Shin and Guodong Zeng and Guoyan Zheng and Vladimir S. Fonov and Andrew Doyle and Yongchao Xu and Pim Moeskops and Josien Pluim and Christian Desrosiers and Ismail Ben Ayed and Gerard Sanroma and Oualid Benkarim and Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and Weili Lin and Gang Li and Dinggang Shen} } @article {aKuijf, title = {Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge}, journal = {IEEE Transactions on Medical Imaging}, year = {2019}, month = {03/2019}, abstract = {

Quantification of white matter hyperintensities (WMH) of presumed vascular origin is of key\ importance in many neurological research studies. Advanced\ measurements are obtained from manual segmentations on brain\ MR images, which is a laborious procedure. Automatic WMH\ segmentation methods exist, but a standardized comparison of\ such methods is lacking. We organized a scientific challenge, in\ which developers could evaluate their method on a standardized\ multi-center/-scanner image dataset, giving an objective comparison:\ the WMH Segmentation Challenge (http://wmh.isi.uu.nl/).\ Sixty T1+FLAIR images from three MR scanners were released\ with manual WMH segmentations. A secret test set of 110\ images from five MR scanners was used for evaluation. Methods\ had to be containerized and submitted to the challenge organizers.Five evaluation metrics were used to rank the methods:\ (1) Dice Similarity Coefficient, (2) modified Hausdorff distance\ (95th percentile), (3) absolute percentage volume difference, (4)\ sensitivity for detecting individual lesions, and (5) F1-score for\ individual lesions. Additionally, methods were ranked on their\ inter-scanner robustness.\ Twenty participants submitted their method for evaluation.\ This paper provides a detailed analysis of the results. In brief,there is a cluster of four methods that rank significantly better\ than the other methods. There is one clear winner, which also\ has the best inter-scanner robustness.\ The challenge remains open for future submissions and provides\ a public platform for method evaluation.

}, keywords = {brain, Evaluation and performance, Magnetic resonance imaging (MRI), segmentation}, issn = {0278-0062}, doi = {10.1109/TMI.2019.2905770}, author = {Hugo Kuijf and Matthijs Biesbroek and Jeroen de Bresser and Rutger Heinen and Simon Andermatt and Mariana Bento and Matt Berseth and Mikhail Belyaev and Jorge Cardoso and Adri{\`a} Casamitjana and Louis Collins and Mahsa Dadar and Achileas Georgiou and Mohsen Ghafoorian and Dakai Jin and April Khademi and Jesse Knight and Hongwei Li and Xavier Llado and Miguel Luna and Qaiser Mahmood and Richard McKinley and Alireza Mehrtash and Sebastien Ourselin and Bo-yong Park and Hyunkin Park and Sang Hyun Park and Simon Pezold and Elodie Puybareau and Leticia Rittner and Carole Sudre and Sergi Valverde and Ver{\'o}nica Vilaplana and Rolan Wiest and Yongchao Xu and Ziyue Xu and Guodong Zeng and Jianguo Zhang and Guoyan Zheng and Christoper Chen and Wiesje van der Flier and Frederik Barkhof and Max Viergever and Geert Jan Biessels} }