Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge

Sun, Yue; Gao, Kun; Wu, Zhengwang; Lei, Zhihao; Wei, Ying; Ma, Jun; Yang, Xiaoping; Xue, Feng; Zhao, Li; Phan, Trung Le; Shin, Jitae; Zhong, Tao; Zhang, Yu; Yu, Lequan; Li, Caizi; Basnet, Ramesh; Ahmad, M. Omair; Swamy, M. N. S.; Ma, Wenao; Dou, Qi; Bui, Toan Duc; Noguera, Camilo Bermudez; Landman, Bennett A.; Gotlib, Ian H.; Humphreys, Kathryn L.; Shultz, Sarah; Li, Longchuan; Niu, Sijie; Lin, Weili; Jewells, Valerie; Li, Gang; Shen, Dinggang; Wang, Li

doi:10.1109/tmi.2021.3055428

Cited by 58 publications

(26 citation statements)

References 39 publications

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“…Table 1 shows that there were large differences between the two datasets in terms of scanners and imaging parameters, resulting in different image contrasts/appearances/patterns, as shown in Figure 3. These results emphasize that models trained on one dataset with a set of specific imaging parameters tend to perform poorly for other datasets with different imaging parameters (protocols/scanners), which is consistent with previous observations (Sun et al, 2021).…”

Section: Resultssupporting

confidence: 91%

Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imaging

et al. 2021

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Autism, or autism spectrum disorder (ASD), is a developmental disability that is diagnosed at about 2 years of age based on abnormal behaviors. Existing neuroimaging-based methods for the prediction of ASD typically focus on functional magnetic resonance imaging (fMRI); however, most of these fMRI-based studies include subjects older than 5 years of age. Due to challenges in the application of fMRI for infants, structural magnetic resonance imaging (sMRI) has increasingly received attention in the field for early status prediction of ASD. In this study, we propose an automated prediction framework based on infant sMRI at about 24 months of age. Specifically, by leveraging an infant-dedicated pipeline, iBEAT V2.0 Cloud, we derived segmentation and parcellation maps from infant sMRI. We employed a convolutional neural network to extract features from pairwise maps and a Siamese network to distinguish whether paired subjects were from the same or different classes. As compared to T1w imaging without segmentation and parcellation maps, our proposed approach with segmentation and parcellation maps yielded greater sensitivity, specificity, and accuracy of ASD prediction, which was validated using two datasets with different imaging protocols/ scanners and was confirmed by receiver operating characteristic analysis. Furthermore, comparison with state-of-the-art methods demonstrated the superior effectiveness and robustness of the proposed method. Finally, attention maps were generated to identify subject-specific autism effects, supporting the reasonability of the predictive results. Collectively, these findings demonstrate the utility of our unified framework for the early-stage status prediction of ASD by sMRI. Lay SummaryThe status prediction of autism spectrum disorder (ASD) at an early age is highly desirable, as early intervention may significantly reduce autism symptoms. However, current methods for diagnosing young children are limited to behavioral assays. In this study, we propose an automated method for ASD status prediction at the age of 24 months that uses infant structural magnetic resonance imaging to identify neural features.

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Section: Resultssupporting

confidence: 91%

Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imaging

et al. 2021

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“…As a result, many researchers rely on smaller proprietary datasets, making it challenging to show the full potential of their algorithms and even more challenging to compare their results against other published methods. Medical image analysis challenges (Menze et al, 2015;Simpson et al, 2019;Kurc et al, 2020;Orlando et al, 2020;Codella et al, 2019;Bernard et al, 2018;Sun et al, 2021;Heller et al, 2021;, therefore, play a pivotal role in the development of machine learning algorithms for medical image analysis by making large-scale, carefully labeled, multi-center, real-world datasets publicly available for training, testing, and comparing machine learning algorithms. In particular, the Brain Tumor Segmentation (BraTS) challenge has provided the community with a benchmarking platform to compare segmentation methods for over ten years, increasing the dataset size each year (Menze et al, 2015;Bakas et al, 2017c.…”

Section: Introductionmentioning

confidence: 99%

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation -- Analysis of Ranking Metrics and Benchmarking Results

Mehta¹,

Filos²,

Baid³

et al. 2021

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Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing metrics to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a metric developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This metric (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS.

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“…To promote the development of 6-month-old infant brain MRI segmentation, the MICCAI iSeg grand challenge was established (iSeg-2017/2019) and has become a benchmark in this field (Sun et al, 2021;Wang et al, 2019b).…”

Section: Deep Learning-based Methods That Automatically Extract Effective Hierarchy Features Improve Resultsmentioning

confidence: 99%

3D-MASNet: 3D Mixed-scale Asymmetric Convolutional Segmentation Network for 6-month-old Infant Brain MR Images

Zeng

Zhao

Sun

et al. 2021

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Precise segmentation of infant brain MR images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is essential for studying neuroanatomical hallmarks of early brain development. However, for 6-month-old infants, the extremely low-intensity contrast caused by inherent myelination hinders accurate tissue segmentation. Existing convolutional neural networks (CNNs) based segmentation model for this task generally employ single-scale symmetric convolutions, which are inefficient for encoding the isointense tissue boundaries in limited samples of baby brain images. Here, we propose a 3D mixed-scale asymmetric convolutional segmentation network (3D-MASNet) framework for brain MR images of 6-month-old infant. We replaced the traditional convolutional layer of an existing to-be-trained network with a 3D mixed-scale convolution block consisting of asymmetric kernels (MixACB) during the training phase and then equivalently converted it into the original network. Five canonical CNN segmentation models were evaluated using both T1- and T2-weighted images of 23 6-month-old infants from iSeg-2019 datasets, which contained manual labels as ground truth. MixACB significantly enhanced the average accuracy of all five models and obtained the largest improvement in the fully convolutional network model (CC-3D-FCN) and the highest performance in the Dense U-Net model. This approach further obtained Dice coefficient accuracies of 0.931, 0.912, and 0.961 in GM, WM, and CSF, respectively, ranking first among 30 teams on the validation dataset of the iSeg-2019 Grand Challenge. Thus, the proposed 3D-MASNet can improve the accuracy of existing CNNs-based segmentation models as a plug-and-play solution that offers a promising technique for future infant brain MRI studies.

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Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge

Cited by 58 publications

References 39 publications

Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imaging

Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imaging

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation -- Analysis of Ranking Metrics and Benchmarking Results

3D-MASNet: 3D Mixed-scale Asymmetric Convolutional Segmentation Network for 6-month-old Infant Brain MR Images

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