Intracranial hemorrhage detection by 3D voxel segmentation on brain CT images

Sun, Mingjie; Hu, Rong; Yu, Huimin; Zhao, Boyang; Ren, Huipeng

doi:10.1109/wcsp.2015.7341238

Cited by 19 publications

(18 citation statements)

References 17 publications

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“…After the initial success of deep learning [ 10 ] in object recognition from images [ 3 , 11 ], deep neural networks have been adopted for a broad range of tasks in medical imaging, ranging from cell segmentation [ 12 ] and cancer detection [ 13 , 14 , 15 , 16 , 17 ] to intracranial hemorrhage detection [ 5 , 8 , 18 , 19 , 20 , 21 , 22 ] and CT/MRI super-resolution [ 23 , 24 , 25 , 26 ]. Since we address the task of intracranial hemorrhage detection, we consider related works that are focused on the same task as ours [ 5 , 6 , 7 , 8 , 18 , 19 , 20 , 21 , 22 , 27 , 28 , 29 , 30 ], as well as works that study intracranial hemorrhage segmentation [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ].…”

Section: Related Workmentioning

confidence: 99%

“…While most of the recent works proposed deep learning approaches such as convolutional neural networks [ 18 , 20 , 21 , 22 , 27 , 29 , 30 , 37 ], fully-convolutional networks (FCNs) [ 19 , 32 , 33 , 36 , 38 , 39 ] and hybrid convolutional and recurrent models [ 5 , 6 , 7 , 8 ], there are still some recent works based on conventional machine learning methods, e.g., superpixels [ 43 , 44 ], fuzzy C-means [ 31 , 35 ], level set [ 42 , 43 ], histogram analysis [ 41 ], thresholding [ 40 ] and continuous max-flow [ 34 ].…”

Section: Related Workmentioning

confidence: 99%

“…Pszczolkowski et al [ 40 ] used the mean and the median intensity in T2-weighted MRI to compute a segmentation threshold, while Ray et al [ 41 ] analyzed the density of pixels at different levels of intensity to find the desired threshold. Some methods [ 43 , 44 ] partitioned the 2D or 3D images into small segments known as superpixels or supervoxels, respectively. Sun et al [ 44 ] obtained supervoxels by using C-means clustering and by removing the smaller clusters.…”

Section: Related Workmentioning

confidence: 99%

“…Some methods [ 43 , 44 ] partitioned the 2D or 3D images into small segments known as superpixels or supervoxels, respectively. Sun et al [ 44 ] obtained supervoxels by using C-means clustering and by removing the smaller clusters. Soltaninejad et al [ 43 ] applied their method on both 2D or 3D images, grouping the superpixels and supervoxels based on their average intensity.…”

Section: Related Workmentioning

confidence: 99%

“…They used an edge indicator for regularization. The methods based on statistical or standard machine learning approaches presented so far [ 31 , 34 , 35 , 40 , 41 , 42 , 43 , 44 ] are not suitable for large-scale datasets, requiring manual adjustment of parameters and careful monitoring of the results. Indeed, Chung et al [ 34 ] acknowledge that such methods can only be used in a semi-automatic scenario.…”

Section: Related Workmentioning

confidence: 99%

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Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks

Burduja

Ionescu

Verga

2020

Sensors

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In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed system is based on a lightweight deep neural network architecture composed of a convolutional neural network (CNN) that takes as input individual CT slices, and a Long Short-Term Memory (LSTM) network that takes as input multiple feature embeddings provided by the CNN. For efficient processing, we consider various feature selection methods to produce a subset of useful CNN features for the LSTM. Furthermore, we reduce the CT slices by a factor of 2×, which enables us to train the model faster. Even if our model is designed to balance speed and accuracy, we report a weighted mean log loss of 0.04989 on the final test set, which places us in the top 30 ranking (2%) from a total of 1345 participants. While our computing infrastructure does not allow it, processing CT slices at their original scale is likely to improve performance. In order to enable others to reproduce our results, we provide our code as open source. After the challenge, we conducted a subjective intracranial hemorrhage detection assessment by radiologists, indicating that the performance of our deep model is on par with that of doctors specialized in reading CT scans. Another contribution of our work is to integrate Grad-CAM visualizations in our system, providing useful explanations for its predictions. We therefore consider our system as a viable option when a fast diagnosis or a second opinion on intracranial hemorrhage detection are needed.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks

Burduja

Ionescu

Verga

2020

Sensors

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An efficient stacked bidirectionalGRU‐LSTMnetwork for intracranial hemorrhage detection

Kothala,

Guntur

2023

Int J Imaging Syst Tech

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Intracranial hemorrhage (ICH) is a dangerous condition that needs prompt diagnosis and treatment. Computed tomography (CT) images are employed in examination of individuals with ICH, which produces better results and cost‐effective than MRI. The existing convolutional neural network (CNN) models are unable to consider inter‐pixel dependency, which leads to false predictions while considering the input CT Images. In this study, we implemented an efficient model of a stack of bidirectional gated recurrent unit (Bi‐GRU) with a bidirectional long short‐term memory (Bi‐LSTM) based CNN to improve detection accuracy in the case of 2D slices. The proposed model holds slice‐wise information by accessing the properties of both Bi‐LSTM and Bi‐GRU modules in a single unit. As a result, the model attained a testing and training accuracy of 96.2% and 93.4%, respectively, with a test loss score of 0.126. In addition, the proposed model could outperform the state‐of‐the‐art CNN in identifying brain hemorrhages.

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Medical lesion segmentation by combining multimodal images with modality weighted UNet

Zhu

et al. 2022

Medical Physics

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Purpose: Automatic segmentation of medical lesions is a prerequisite for efficient clinic analysis. Segmentation algorithms for multimodal medical images have received much attention in recent years. Different strategies for multimodal combination (or fusion), such as probability theory, fuzzy models, belief functions, and deep neural networks, have also been developed. In this paper, we propose the modality weighted UNet (MW-UNet) and attention-based fusion method to combine multimodal images for medical lesion segmentation. Methods: MW-UNet is a multimodal fusion method which is based on UNet, but we use a shallower layer and fewer feature map channels to reduce the amount of network parameters, and our method uses the new multimodal fusion method called fusion attention. It uses weighted sum rule and fusion attention to combine feature maps in intermediate layers. During training, all the weight parameters are updated through backpropagation like other parameters in the network. We also incorporate residual blocks into MW-UNet to further improve segmentation performance. The comparison between the automatic multimodal lesion segmentations and the manual contours was quantified by (1) five metrics including Dice, 95% Hausdorff Distance (HD95), volumetric overlap error (VOE), relative volume difference (RVD), and mean-Intersection-over-Union (mIoU); (2) Number of parameters and flops to calculate the complexity of the network. Results: The proposed method is verified on ZJCHD, which is the data set of contrast-enhanced computed tomography (CECT) for Liver Lesion Segmentation taken from Cancer Hospital of the

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Intracranial hemorrhage detection by 3D voxel segmentation on brain CT images

Cited by 19 publications

References 17 publications

Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks

Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks

An efficient stacked bidirectionalGRU‐LSTMnetwork for intracranial hemorrhage detection

Medical lesion segmentation by combining multimodal images with modality weighted UNet

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