Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss

Wang, Guotai; Shapey, Jonathan; Li, Wenqi; Dorent, Reuben; Dimitriadis, Alexis; Bisdas, Sotirios; Paddick, Ian; Bradford, Robert; Zhang, Shaoting; Ourselin, Sébastien; Vercauteren, Tom

doi:10.1007/978-3-030-32245-8_30

Cited by 53 publications

(48 citation statements)

References 11 publications

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“…Then, to investigate the efficacy of deep supervision, we trained our model with different supervision strategies. Specifically, we compared training with supervision at: 1) only the final network output, 2) both the final output and the multi-scale features, and 3) the attentive maps instead of attentive features in an approach similar to [57].…”

Section: A Alternative Techniquesmentioning

confidence: 99%

A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI

Dou

Karimi

Rollins

et al. 2021

IEEE Trans. Med. Imaging

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Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding. Manual segmentation of the cortical plate, or manual refinement of automatic segmentations is tedious and time-consuming. Automatic segmentation of the cortical plate, on the other hand, is challenged by the relatively low resolution of the reconstructed fetal brain MRI scans compared to the thin structure of the cortical plate, partial voluming, and the wide range of variations in the morphology of the cortical plate as the brain matures during gestation. To reduce the burden of manual refinement of segmentations, we have developed a new and powerful deep learning segmentation method. Our method exploits new deep attentive modules with mixed kernel convolutions within a fully

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Section: A Alternative Techniquesmentioning

confidence: 99%

A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI

Dou

Karimi

Rollins

et al. 2021

IEEE Trans. Med. Imaging

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“…We have previously developed a novel artificial intelligence (AI) framework based on a 2.5D convolutional neural network (CNN) able to exploit the different in-plane and through-plane resolutions encountered in standard clinical imaging protocols 9,10 . In particular, we embedded a computational attention module to enable the CNN to focus on the small VS target and included a supervision on the attention map for more accurate segmentation.…”

Section: Background and Summarymentioning

confidence: 99%

“…A comparison between the 2.5D UNet on which our implementation is based and a 3D UNet as well as other baseline neural networks without attention module and/or hardness-weighting was published in, 9 showing improvements in Dice score of more than 3%.…”

Section: Technical Validationmentioning

confidence: 99%

Segmentation of vestibular schwannoma from MRI — An open annotated dataset and baseline algorithm

Shapey

Kujawa

Dorent

et al. 2021

Preprint

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Automatic segmentation of vestibular schwannomas (VS) from magnetic resonance imaging (MRI) could significantly improve clinical workflow and assist patient management. We have previously developed a novel artificial intelligence framework based on a 2.5D convolutional neural network achieving excellent results equivalent to those achieved by an independent human annotator. Here, we provide the first publicly-available annotated imaging dataset of VS by releasing the data and annotations used in our prior work. This collection contains a labelled dataset of 484 MR images collected on 242 consecutive patients with a VS undergoing Gamma Knife Stereotactic Radiosurgery at a single institution. Data includes all segmentations and contours used in treatment planning and details of the administered dose. Implementation of our automated segmentation algorithm uses MONAI, a freely-available open-source framework for deep learning in healthcare imaging. These data will facilitate the development and validation of automated segmentation frameworks for VS and may also be used to develop other multi-modal algorithmic models.

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“…In addition, studies have demonstrated that using multiparametric MR images improves detection for brain tumors with multiple subregions [14][15][16]. For VS tumor detection, Wang et al [17] and Shapey et al [18] demonstrated the feasibility of CNN and multiparametric MR images to the segmentation of VS lesions. Lee et al [19] improved VS lesion detection by treating multiparametric MR images from the same patient as different channels of the same image.…”

Section: Introductionmentioning

confidence: 99%

Detection of Vestibular Schwannoma on Triple-parametric Magnetic Resonance Images Using Convolutional Neural Networks

Huang¹,

Lee

et al. 2021

J. Med. Biol. Eng.

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Purpose The first step in typical treatment of vestibular schwannoma (VS) is to localize the tumor region, which is time-consuming and subjective because it relies on repeatedly reviewing different parametric magnetic resonance (MR) images. A reliable, automatic VS detection method can streamline the process. Methods A convolutional neural network architecture, namely YOLO-v2 with a residual network as a backbone, was used to detect VS tumors from MR images. To heighten performance, T1-weighted–contrast-enhanced, T2-weighted, and T1-weighted images were combined into triple-channel images for feature learning. The triple-channel images were cropped into three sizes to serve as input images of YOLO-v2. The VS detection effectiveness levels were evaluated for two backbone residual networks that downsampled the inputs by 16 and 32. Results The results demonstrated the VS detection capability of YOLO-v2 with a residual network as a backbone model. The average precision was 0.7953 for a model with 416 × 416-pixel input images and 16 instances of downsampling, when both the thresholds of confidence score and intersection-over-union were set to 0.5. In addition, under an appropriate threshold of confidence score, a high average precision, namely 0.8171, was attained by using a model with 448 × 448-pixel input images and 16 instances of downsampling. Conclusion We demonstrated successful VS tumor detection by using a YOLO-v2 with a residual network as a backbone model on resized triple-parametric MR images. The results indicated the influence of image size, downsampling strategy, and confidence score threshold on VS tumor detection.

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Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss

Cited by 53 publications

References 11 publications

A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI

A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI

Segmentation of vestibular schwannoma from MRI — An open annotated dataset and baseline algorithm

Detection of Vestibular Schwannoma on Triple-parametric Magnetic Resonance Images Using Convolutional Neural Networks

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