A large, open source dataset of stroke anatomical brain images and manual lesion segmentations

Liew, Sook‐Lei; Anglin, Julia; Banks, Nick W.; Sondag, Matt; Ito, Kaori; Kim, Ho‐Sung; Chan, Jennifer; Ito, Joyce; Jung, Connie; Khoshab, Nima; Lefebvre, Stéphanie; Nakamura, William; Saldaña, David; Schmiesing, Allie; Tran, Cathy; Vo, Danny; Ard, Tyler; Heydari, Panthea; Kim, Bokkyu; Aziz‐Zadeh, Lisa; Cramer, Steven C.; Liu, Jingchun; Soekadar, Surjo R.; Nordvik, Jan Egil; Westlye, Lars T.; Wang, Junping; Winstein, Carolee J.; Yu, Chunshui; Ai, Lei; Koo, Bonhwang; Craddock, R. Cameron; Milham, Michael P.; Lakich, Matthew; Pienta, Amy; Stroud, Alison

doi:10.1038/sdata.2018.11

Cited by 207 publications

(99 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We obtained our stroke dataset from the Anatomical Tracings of Lesions After Stroke (ATLAS) database Figure S1, Supporting Information for lesion volume histogram). Further information on image acquisition for stroke data can be found in Liew et al, 2018.…”

Section: Stroke Datamentioning

confidence: 99%

“…Interrater and intrarater reliability was computed for five stroke lesions (interrater DC: 0.75 ± 0.18; intrarater DC: 0.83 ± 0.13;Liew et al, 2018). Individuals tracing lesions consisted of undergraduate students, graduate students, and postdoctoral fellows.…”

mentioning

confidence: 99%

“…either the coronal or axial view in MRIcron (http://people.cas.sc.edu/ rorden/mricron/index.html) with a mouse, track pad, or a tablet (depending on preference) by 1 of 11 trained individuals. For further information on the labeling and training protocol, seeLiew et al, 2018. Standardized training included utilization of a detailed protocol and instructional video, and guidance with extensive feedback on lesion tracings from an expert tracer and in consultation with a neuroradiologist.…”

mentioning

confidence: 99%

“…All lesion locations were reviewed by a neuroradiologist. Interrater and intrarater reliability was computed for five stroke lesions (interrater DC: 0.75 ± 0.18; intrarater DC: 0.83 ± 0.13;Liew et al, 2018). For further information on the labeling and training protocol, seeLiew et al, 2018.…”

mentioning

confidence: 99%

See 3 more Smart Citations

A comparison of automated lesion segmentation approaches for chronic stroke T1‐weighted MRI data

Ito

Kim

Liew

2019

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

Accurate stroke lesion segmentation is a critical step in the neuroimaging processing pipeline for assessing the relationship between poststroke brain structure, function, and behavior. Many multimodal segmentation algorithms have been developed for acute stroke neuroimaging, yet few algorithms are effective with only a single T1‐weighted (T1w) anatomical MRI. This is a critical gap because multimodal MRI is not commonly available due to time and cost constraints in the stroke rehabilitation setting. Although several attempts to automate the segmentation of chronic lesions on single‐channel T1w MRI have been made, these approaches have not been systematically evaluated on a large dataset. We performed an exhaustive review of the literature and identified one semiautomated and three fully automated approaches for segmentation of chronic stroke lesions using T1w MRI within the last 10 years: Clusterize, automated lesion identification (ALI), Gaussian naïve Bayes lesion detection (lesionGnb), and lesion identification with neighborhood data analysis (LINDA). We evaluated each method on a large T1w stroke dataset (N = 181). LINDA was the most computationally expensive approach, but performed best across the three main evaluation metrics (median values: dice coefficient = 0.50, Hausdorff's distance = 36.34 mm, and average symmetric surface distance = 4.97 mm). lesionGnb had the highest recall/least false negatives (median = 0.80). However, across the automated methods, many lesions were either misclassified (ALI: 28, lesionGnb: 39, LINDA: 45) or not identified (ALI: 24, LINDA: 23, lesionGnb: 0). Segmentation accuracy in all automated methods were influenced by size (small: worst) and stroke territory (brainstem, cerebellum: worst) of the lesion. To facilitate reproducible science, our analysis files have been made publicly available online.

show abstract

Section: Stroke Datamentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

A comparison of automated lesion segmentation approaches for chronic stroke T1‐weighted MRI data

Ito

Kim

Liew

2019

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our study sought to expand on previous findings (Khlif et al 2019;Nogovitsyn et al, 2019) and evaluate how Hippodeep compares to previously tested methods for hippocampal segmentation in a stroke population. Using the Anatomical Tracings of Lesions After Stroke dataset (ATLAS; Liew et al, 2018), we compared Hippodeep, FreeSurfer version 6.0 gross hippocampal segmentation, and FreeSurfer version 6.0 'sum of subfields' segmentation in terms of 1) output failure rates and 2) accuracy when compared to expert manual segmentations. We hypothesized that Hippodeep's CNNbased method would perform better on lesioned brain anatomy, resulting in fewer segmentation failures and more accurate hippocampal segmentations than either FreeSurfer method.…”

mentioning

confidence: 99%

Testing a convolutional neural network-based hippocampal segmentation method in a stroke population

Zavaliangos‐Petropulu

Tubi

Haddad

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

As stroke mortality rates decrease, there has been a surge of effort to study post-stroke dementia (PSD) to improve long-term quality of life for stroke survivors. Hippocampal volume may be an important neuroimaging biomarker in post-stroke dementia, as it has been associated with many other forms of dementia. However, studying hippocampal volume using MRI requires hippocampal segmentation. Advances in automated segmentation methods have allowed for studying the hippocampus on a large scale, which is important for robust results in the heterogeneous stroke population. However, most of these automated methods use a single atlas-based approach and may fail in the presence of severe structural abnormalities common in stroke. Hippodeep, a new convolutional neural network-based hippocampal segmentation method, does not rely solely on a single atlas-based approach and thus may be better suited for stroke populations. Here, we compared quality control and the accuracy of segmentations generated by Hippodeep and two well-accepted hippocampal segmentation methods on stroke MRIs (FreeSurfer 6.0 whole hippocampus and FreeSurfer 6.0 sum of hippocampal subfields). Quality control was performed using a stringent protocol for visual inspection of the segmentations, and accuracy was measured as volumetric and spatial comparisons to the manual segmentations. Hippodeep performed significantly better than both FreeSurfer methods in terms of quality control and spatial accuracy. Overall, this study suggests that both Hippodeep and FreeSurfer may be useful for hippocampal segmentation in stroke rehabilitation research, but Hippodeep may be more robust to stroke lesion anatomy.

show abstract

MSSA‐Net: A novel multi‐scale feature fusion and global self‐attention network for lesion segmentation

Huang

Zhang

Zhang³

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

In medical image segmentation tasks, it is typical to adopt convolutional neural networks with a serial encoder-decoder structure. However, mainstream networks cannot simultaneously achieve sufficient extraction of global features and the fusion of multi-scale information, which may lead to unpromising results for the segmentation of pathological images. Therefore, this article proposed a novel multi-scale feature fusion and global self-attention network (MSSA-Net) for medical image segmentation. Specifically, we designed a parallel double-encoder network with a multi-scale feature fusion encoder (MS-Encoder) and a self-attention encoder (SA-Encoder). The SA-Encoder introduces the transformer's global self-attention mechanism to extract global features, and the MS-Encoder adopts atrous spatial pyramid pooling (ASPP) to realize multi-scale fusion. We have evaluated the proposed MSSA-Net using three medical segmentation datasets, covering various imaging modalities such as colonoscopy and magnetic resonance imaging. Experiments on the CVC-ClinicDC, the 2015 MICCAI subchallenge on automatic polyp detection dataset, and anatomical tracings of lesions after stroke (ATLAS) show that our MSSA-Net outperforms mainstream methods such as DoubleU-Net and TransUNet. Moreover, MSSA-Net can predict more accurate segmentation masks, especially in the case of ATLAS, which has challenging images such as multiple shadow areas and discrete lesions.

show abstract

A large, open source dataset of stroke anatomical brain images and manual lesion segmentations

Cited by 207 publications

References 31 publications

A comparison of automated lesion segmentation approaches for chronic stroke T1‐weighted MRI data

A comparison of automated lesion segmentation approaches for chronic stroke T1‐weighted MRI data

Testing a convolutional neural network-based hippocampal segmentation method in a stroke population

MSSA‐Net: A novel multi‐scale feature fusion and global self‐attention network for lesion segmentation

Contact Info

Product

Resources

About