Lesion synthesis to improve intracranial hemorrhage detection and classification for CT images

Zhang, Guyue; Chen, Kaixing; Xu, Shangliang; Cho, Po Chuan; Nan, Yang; Zhou, Xin; Lv, Chuanfeng; Li, Changsheng

doi:10.1016/j.compmedimag.2021.101929

Cited by 20 publications

(4 citation statements)

References 21 publications

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“…Here, different datasets (labeled and unlabeled) were used for training, validation, and testing which produced varied results for each dataset. The work in [17] proposed a technique to generate additional labeled training examples that were utilized to produce an artificial lesion on a non-lesion CT slice and generate the result, particularly for microbleeds. An artificial mask generator generates artificial masks of any size and shape at any location and then converts them into hemorrhagic lesions through a lesion synthesis network.…”

Section: Related Workmentioning

confidence: 99%

A Double-Branch Xception Architecture for Acute Hemorrhage Detection and Subtype Classification

Naeem Akram,

Usman Yaseen,

Waqar

et al. 2023

Computers, Materials &Amp; Continua

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This study presents a deep learning model for efficient intracranial hemorrhage (ICH) detection and subtype classification on non-contrast head computed tomography (CT) images. ICH refers to bleeding in the skull, leading to the most critical life-threatening health condition requiring rapid and accurate diagnosis. It is classified as intra-axial hemorrhage (intraventricular, intraparenchymal) and extra-axial hemorrhage (subdural, epidural, subarachnoid) based on the bleeding location inside the skull. Many computer-aided diagnoses (CAD)-based schemes have been proposed for ICH detection and classification at both slice and scan levels. However, these approaches perform only binary classification and suffer from a large number of parameters, which increase storage costs. Further, the accuracy of brain hemorrhage detection in existing models is significantly low for medically critical applications. To overcome these problems, a fast and efficient system for the automatic detection of ICH is needed. We designed a double-branch model based on xception architecture that extracts spatial and instant features, concatenates them, and creates the 3D spatial context (common feature vectors) fed to a decision tree classifier for final predictions. The data employed for the experimentation was gathered during the 2019 Radiologist Society of North America (RSNA) brain hemorrhage detection challenge. Our model outperformed benchmark models and achieved better accuracy in intraventricular (99.49%), subarachnoid (99.49%), intraparenchymal (99.10%), and subdural (98.09%) categories, thereby justifying the performance of the proposed double-branch xception architecture for ICH detection and classification.

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Section: Related Workmentioning

confidence: 99%

A Double-Branch Xception Architecture for Acute Hemorrhage Detection and Subtype Classification

Naeem Akram,

Usman Yaseen,

Waqar

et al. 2023

Computers, Materials &Amp; Continua

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“…However, the growing number of CT scans received by medical facilities can often lead to delays in diagnosis due to limited access to specialized radiologists, especially in academic institutions [4,5]. As a potential solution to address this challenge, an automatic notification system employing artificial intelligence (AI) methods has been proposed for efficient and timely brain hemorrhage detection [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Radiomics and Deep Features: Robust Classification of Brain Hemorrhages and Reproducibility Analysis Using a 3D Autoencoder Neural Network

Bijari,

Sayfollahi,

Mardokh-Rouhani

et al. 2024

Bioengineering

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This study evaluates the reproducibility of machine learning models that integrate radiomics and deep features (features extracted from a 3D autoencoder neural network) to classify various brain hemorrhages effectively. Using a dataset of 720 patients, we extracted 215 radiomics features (RFs) and 15,680 deep features (DFs) from CT brain images. With rigorous screening based on Intraclass Correlation Coefficient thresholds (>0.75), we identified 135 RFs and 1054 DFs for analysis. Feature selection techniques such as Boruta, Recursive Feature Elimination (RFE), XGBoost, and ExtraTreesClassifier were utilized alongside 11 classifiers, including AdaBoost, CatBoost, Decision Trees, LightGBM, Logistic Regression, Naive Bayes, Neural Networks, Random Forest, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN). Evaluation metrics included Area Under the Curve (AUC), Accuracy (ACC), Sensitivity (SEN), and F1-score. The model evaluation involved hyperparameter optimization, a 70:30 train–test split, and bootstrapping, further validated with the Wilcoxon signed-rank test and q-values. Notably, DFs showed higher accuracy. In the case of RFs, the Boruta + SVM combination emerged as the optimal model for AUC, ACC, and SEN, while XGBoost + Random Forest excelled in F1-score. Specifically, RFs achieved AUC, ACC, SEN, and F1-scores of 0.89, 0.85, 0.82, and 0.80, respectively. Among DFs, the ExtraTreesClassifier + Naive Bayes combination demonstrated remarkable performance, attaining an AUC of 0.96, ACC of 0.93, SEN of 0.92, and an F1-score of 0.92. Distinguished models in the RF category included SVM with Boruta, Logistic Regression with XGBoost, SVM with ExtraTreesClassifier, CatBoost with XGBoost, and Random Forest with XGBoost, each yielding significant q-values of 42. In the DFs realm, ExtraTreesClassifier + Naive Bayes, ExtraTreesClassifier + Random Forest, and Boruta + k-NN exhibited robustness, with 43, 43, and 41 significant q-values, respectively. This investigation underscores the potential of synergizing DFs with machine learning models to serve as valuable screening tools, thereby enhancing the interpretation of head CT scans for patients with brain hemorrhages.

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“…Zhang et al. 12 employed partial convolution to generate synthetic hemorrhage lesions for improved intracranial hemorrhage diagnosis using cGAN network.…”

Section: Introductionmentioning

confidence: 99%

“…Dong et al 10 utilized 3D partial convolution to reconstruct the missing regions in ultrasound images using least squares generative adversarial network 11 (LSGAN) network. Zhang et al 12 employed partial convolution to generate synthetic hemorrhage lesions for improved intracranial hemorrhage diagnosis using cGAN network.…”

Section: Introductionmentioning

confidence: 99%

A partial convolution generative adversarial network for lesion synthesis and enhanced liver tumor segmentation

Liu

Yang

2023

J Applied Clin Med Phys

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Lesion segmentation is critical for clinicians to accurately stage the disease and determine treatment strategy. Deep learning based automatic segmentation can improve both the segmentation efficiency and accuracy. However, training a robust deep learning segmentation model requires sufficient training examples with sufficient diversity in lesion location and lesion size. This study is to develop a deep learning framework for generation of synthetic lesions with various locations and sizes that can be included in the training dataset to enhance the lesion segmentation performance. The lesion synthesis network is a modified generative adversarial network (GAN). Specifically, we innovated a partial convolution strategy to construct a U‐Net‐like generator. The discriminator is designed using Wasserstein GAN with gradient penalty and spectral normalization. A mask generation method based on principal component analysis (PCA) was developed to model various lesion shapes. The generated masks are then converted into liver lesions through a lesion synthesis network. The lesion synthesis framework was evaluated for lesion textures, and the synthetic lesions were used to train a lesion segmentation network to further validate the effectiveness of the lesion synthesis framework. All the networks are trained and tested on the LITS public dataset. Our experiments demonstrate that the synthetic lesions generated by our approach have very similar distributions for the two parameters, GLCM‐energy and GLCM‐correlation. Including the synthetic lesions in the segmentation network improved the segmentation dice performance from 67.3% to 71.4%. Meanwhile, the precision and sensitivity for lesion segmentation were improved from 74.6% to 76.0% and 66.1% to 70.9%, respectively. The proposed lesion synthesis approach outperforms the other two existing approaches. Including the synthetic lesion data into the training dataset significantly improves the segmentation performance.

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Lesion synthesis to improve intracranial hemorrhage detection and classification for CT images

Cited by 20 publications

References 21 publications

A Double-Branch Xception Architecture for Acute Hemorrhage Detection and Subtype Classification

A Double-Branch Xception Architecture for Acute Hemorrhage Detection and Subtype Classification

Radiomics and Deep Features: Robust Classification of Brain Hemorrhages and Reproducibility Analysis Using a 3D Autoencoder Neural Network

A partial convolution generative adversarial network for lesion synthesis and enhanced liver tumor segmentation

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