Ischemic Stroke Lesion Segmentation in CT Perfusion Scans Using Pyramid Pooling and Focal Loss

Abulnaga, S. Mazdak; Rubin, Jonathan

doi:10.1007/978-3-030-11723-8_36

Cited by 43 publications

(25 citation statements)

References 27 publications

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“…Although automatic segmentation of ischemic stroke lesion has been widely studied, most of existing methods were proposed to deal with multi-modal MR images (Maier et al, 2017;Winzeck et al, 2018). Only few works have been reported on ischemic stroke lesion segmentation from CTP images (Gillebert et al, 2014;Yahiaoui and Bessaid, 2016;Abulnaga and Rubin, 2018). Some old-fashion methods such as template-based methods (Gillebert et al, 2014) and fuzzy C-Means (Yahiaoui and Bessaid, 2016) are challenged by the complex appearance of stroke lesions.…”

Section: Introductionmentioning

confidence: 99%

Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks

Wang

Song

Dong

et al. 2020

Medical Image Analysis

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Ischemic stroke lesion segmentation from Computed Tomography Perfusion (CTP) images is important for accurate diagnosis of stroke in acute care units. However, it is challenged by low image contrast and resolution of the perfusion parameter maps, in addition to the complex appearance of the lesion. To deal with this problem, we propose a novel framework based on synthesized pseudo Diffusion-Weighted Imaging (DWI) from perfusion parameter maps to obtain better image quality for more accurate segmentation. Our framework consists of three components based on Convolutional Neural Networks (CNNs) and is trained end-to-end. First, a feature extractor is used to obtain both a low-level and high-level compact representation of the raw spatiotemporal Computed Tomography Angiography (CTA) images. Second, a pseudo DWI generator takes as input the concatenation of CTP perfusion parameter maps and our extracted features to obtain the synthesized pseudo DWI. To achieve better synthesis quality, we propose a hybrid loss function that pays more attention to lesion regions and encourages high-level contextual consistency. Finally, we segment the lesion region from the synthesized pseudo DWI, where the segmentation network is based on switchable normalization and channel calibration for better performance. Experimental results showed that our framework achieved the top performance on ISLES 2018 challenge and: 1) our method using synthesized pseudo DWI outperformed methods segmenting the lesion from perfusion parameter maps directly; 2) the feature extractor exploiting additional spatiotemporal CTA images led to better synthesized pseudo DWI quality and higher segmentation accuracy; and 3) the proposed loss functions and network structure improved the pseudo DWI synthesis and lesion segmentation performance. The proposed framework has a potential for improving diagnosis and treatment of the ischemic stroke where access to real DWI scanning is limited.

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Section: Introductionmentioning

confidence: 99%

Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks

Wang

Song

Dong

et al. 2020

Medical Image Analysis

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“…Therefore, the BCE term is able to control the trade-off between FPs and FNs in the pixelwise segmentation task. In spite of that, the networks with only BCE as loss function are often prone to generate more false positives in the segmentation [20]. The study from [21] has proven that Dice loss yields better performance for one-target segmentation and is able to predict the fine appearance features of the object.…”

Section: Lossmentioning

confidence: 99%

Mandible Segmentation of Dental CBCT Scans Affected by Metal Artifacts Using Coarse-to-Fine Learning Model

Qiu

Wel

Kraeima

et al. 2021

JPM

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Accurate segmentation of the mandible from cone-beam computed tomography (CBCT) scans is an important step for building a personalized 3D digital mandible model for maxillofacial surgery and orthodontic treatment planning because of the low radiation dose and short scanning duration. CBCT images, however, exhibit lower contrast and higher levels of noise and artifacts due to extremely low radiation in comparison with the conventional computed tomography (CT), which makes automatic mandible segmentation from CBCT data challenging. In this work, we propose a novel coarse-to-fine segmentation framework based on 3D convolutional neural network and recurrent SegUnet for mandible segmentation in CBCT scans. Specifically, the mandible segmentation is decomposed into two stages: localization of the mandible-like region by rough segmentation and further accurate segmentation of the mandible details. The method was evaluated using a dental CBCT dataset. In addition, we evaluated the proposed method and compared it with state-of-the-art methods in two CT datasets. The experiments indicate that the proposed algorithm can provide more accurate and robust segmentation results for different imaging techniques in comparison with the state-of-the-art models with respect to these three datasets.

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“… [34] utilized a machine learning algorithm with decision trees and spatial regularization to outline the lesion. [35] introduced the pyramid pooling method to combine global and local contextual information to segment lesions from CT images. [36] equipped 3D deep convolutional network to learn 3D contextual information efficiently.…”

Section: Related Workmentioning

confidence: 99%

Joint segmentation and detection of COVID-19 via a sequential region generation network

Zhang

et al. 2021

Pattern Recognition

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The fast pandemics of coronavirus disease (COVID-19) has led to a devastating influence on global public health. In order to treat the disease, medical imaging emerges as a useful tool for diagnosis. However, the computed tomography (CT) diagnosis of COVID-19 requires experts’ extensive clinical experience. Therefore, it is essential to achieve rapid and accurate segmentation and detection of COVID-19. This paper proposes a simple yet efficient and general-purpose network, called Sequential Region Generation Network (SRGNet), to jointly detect and segment the lesion areas of COVID-19. SRGNet can make full use of the supervised segmentation information and then outputs multi-scale segmentation predictions. Through this, high-quality lesion-areas suggestions can be generated on the predicted segmentation maps, reducing the diagnosis cost. Simultaneously, the detection results conversely refine the segmentation map by a post-processing procedure, which significantly improves the segmentation accuracy. The superiorities of our SRGNet over the state-of-the-art methods are validated through extensive experiments on the built COVID-19 database.

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Ischemic Stroke Lesion Segmentation in CT Perfusion Scans Using Pyramid Pooling and Focal Loss

Cited by 43 publications

References 27 publications

Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks

Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks

Mandible Segmentation of Dental CBCT Scans Affected by Metal Artifacts Using Coarse-to-Fine Learning Model

Joint segmentation and detection of COVID-19 via a sequential region generation network

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