Unsupervised Acute Intracranial Hemorrhage Segmentation With Mixture Models

Kärkkäinen, Kimmo; Fazeli, Shayan; Sarrafzadeh, Majid

doi:10.1109/ichi52183.2021.00029

Cited by 6 publications

(6 citation statements)

References 33 publications

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“…We found several unsupervised approaches that did not require any training samples. A recent work led by Kärkkäinen et al [ 65 ] on segmenting ICH regions from CT images employed a clustering technique, which is a strategy commonly used in unsupervised methodologies. The proposed algorithm, based on the expectation-maximization process, adaptively determined the number of representative clusters, which are groups of pixels that have similar intensity values and are likely to be brain abnormalities.…”

Section: Resultsmentioning

confidence: 99%

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Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

et al. 2022

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Background: The purpose of this study was to conduct a systematic review for understanding the availability and limitations of artificial intelligence (AI) approaches that could automatically identify and quantify computed tomography (CT) findings in traumatic brain injury (TBI). Methods: Systematic review, in accordance with PRISMA 2020 and SPIRIT-AI extension guidelines, with a search of 4 databases (Medline, Embase, IEEE Xplore, and Web of Science) was performed to find AI studies that automated the clinical tasks for identifying and quantifying CT findings of TBI-related abnormalities. Results: A total of 531 unique publications were reviewed, which resulted in 66 articles that met our inclusion criteria. The following components for identification and quantification regarding TBI were covered and automated by existing AI studies: identification of TBI-related abnormalities; classification of intracranial hemorrhage types; slice-, pixel-, and voxel-level localization of hemorrhage; measurement of midline shift; and measurement of hematoma volume. Automated identification of obliterated basal cisterns was not investigated in the existing AI studies. Most of the AI algorithms were based on deep neural networks that were trained on 2- or 3-dimensional CT imaging datasets. Conclusion: We identified several important TBI-related CT findings that can be automatically identified and quantified with AI. A combination of these techniques may provide useful tools to enhance reproducibility of TBI identification and quantification by supporting radiologists and clinicians in their TBI assessments and reducing subjective human factors.

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Section: Resultsmentioning

confidence: 99%

“…•Normal/abnormal [65][66][67] 2D = 2-dimensional, 3D = 3-dimensional, ICH = intracranial hemorrhage, ICP = intracranial pressure, SDH = subdural hemorrhage, TBI = traumatic brain injury.…”

Section: Unsupervisedmentioning

confidence: 99%

Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

et al. 2022

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“…Additionally, further experiments done to compare these two models in a 5-fold cross validation setting reveals that the improvement is statistically significant, with a p-value of 2.3 x 10 -4 (two-sample t-test). [34] 0.523 0.416 SegResNet [35] 0.522 0.411 SwinUnet [12] 0.513 0.472 U-Net [8] 0.458 0.351 FPN [36] 0.390 0.332 EM [6] 0.197 -Overall, the results in Table 1 demonstrates the superiority of two-stage segmentation approaches over single-stage methods. These results provide empirical evidence demonstrating the value of using bounding boxes to narrow the field of view of the segmentation model, helping it to improve segmentation performance.…”

Section: Comparing Single-stage and Double-stage Methodsmentioning

confidence: 91%

“…as ICH. Kärkkäinen et al [6] proposed to run EM algorithm on each CT image voxel to detect voxels that contain ICH, but doing so requires the optimization algorithm to be applied iteratively on every voxel, which is computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Approach to Intracranial Hemorrhage Segmentation From Head CT Images

Rajapakse,

Hung How,

Hao Chan

et al. 2024

IEEE Access

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Intracranial hemorrhage (ICH) is an emergency and a potentially life-threatening condition. Automated segmentation of ICH from head CT images can provide clinicians with volumetric measures that can be used for diagnosis and decision support for treatment procedures. Existing solutions typically involve training deep learning models to perform segmentation directly on the whole CT image. However, datasets with segmentation masks are typically very small in comparison with datasets with bounding boxes. Thus, we propose a two-stage approach that utilizes both bounding boxes and segmentation masks to help improve segmentation performance. In the first stage, ICH regions are detected and localized with bounding boxes surrounding the lesion by using a supervised YOLOv5 object detector. In the second stage, the localized ICH foreground is automatically segmented using TransDeepLab, an attention-based transformer network. Although we utilize both ground-truth bounding boxes and segmentation masks, different datasets can be used to train each stage. There is no requirement for pairing up bounding boxes and segmentation masks to train the model. Since bounding box annotations are available in larger quantities than segmentation masks, our approach allows these large datasets of bounding boxes to be used to improve ICH segmentation performance. On our dataset of segmentation masks, we demonstrated that our proposed twostage YOLOv5 + TransDeepLab model outperformed segmentation methods such as SegResNet by 8% in terms of Dice score. Given ground truth bounding boxes, a Dice score of 0.769 is achieved, outperforming state-of-the-art methods such as nnU-Net. In sum, our proposed two-stage approach produces more accurate binary segmentation of ICH for neuroradiologists and these improved measurements could potentially aid their clinical decision making process.

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“…The authors reported an average pixel accuracy of around 84% on the CQ500 dataset to citechilamkurthy2018development. An unsupervised approach was presented in which mixture models were trained on CT scan images [17]. The authors stated that their algorithm utilizes the fact that the properties of haemorrhage and healthy tissues follow different distributions, and therefore an appropriate formulation of these distributions allows the separation of haemorrhages through an Expectation-Maximization process.…”

Section: Literature Reviewmentioning

confidence: 99%

A Graphical Approach For Brain Haemorrhage Segmentation

Mehendale¹,

Gupta²,

Rajadhyaksha³

et al. 2022

Preprint

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Haemorrhaging of the brain is the leading cause of death in people between the ages of 15 and 24 and the third leading cause of death in people older than that. Computed tomography (CT) is an imaging modality used to diagnose neurological emergencies, including stroke and traumatic brain injury. Recent advances in deep learning and image processing have utilised different modalities like CT scans to help automate the detection and segmentation of brain haemorrhage occurrences. In this paper, we propose a novel implementation of an architecture consisting of traditional Convolutional Neural Networks(CNN) along with Graph Neural Networks(GNN) to produce a holistic model for the task of brain haemorrhage segmentation. GNN's work on the principle of neighbourhood aggregation thus providing a reliable estimate of global structures present in images. GNN's work with few layers thus in turn requiring fewer parameters to work with. We were able to achieve a dice coefficient score of around 0.81 with limited data with our implementation.

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Unsupervised Acute Intracranial Hemorrhage Segmentation With Mixture Models

Cited by 6 publications

References 33 publications

Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

Two-Stage Approach to Intracranial Hemorrhage Segmentation From Head CT Images

A Graphical Approach For Brain Haemorrhage Segmentation

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