A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images.

Autor: Qu J; School of Biomedical Engineering, Capital Medical University, Beijing, China.; Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, China.; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, China., Niu H; Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.; Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China., Li Y; School of Biomedical Engineering, Capital Medical University, Beijing, China.; Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, China.; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, China., Chen T; School of Biomedical Engineering, Capital Medical University, Beijing, China.; Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, China.; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, China., Peng F; Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.; Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China., Xia J; Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.; Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China., He X; Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.; Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China., Xu B; Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.; Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China., Chen X; Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.; Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China., Li R; Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China., Liu A; Beijing Neurosurgical Institute, Capital Medical University, Beijing, China. liuaihuadoctor@163.com.; Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. liuaihuadoctor@163.com., Zhang X; School of Biomedical Engineering, Capital Medical University, Beijing, China. zhangxu@ccmu.edu.cn.; Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, China. zhangxu@ccmu.edu.cn.; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, China. zhangxu@ccmu.edu.cn., Li C; School of Biomedical Engineering, Capital Medical University, Beijing, China. lichunlin1981@163.com.; Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, China. lichunlin1981@163.com.; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, China. lichunlin1981@163.com.
Jazyk: angličtina
Zdroj: European radiology [Eur Radiol] 2024 May; Vol. 34 (5), pp. 2838-2848. Date of Electronic Publication: 2023 Oct 16.
DOI: 10.1007/s00330-023-10295-x
Abstrakt: Objectives: To design a deep learning-based framework for automatic segmentation and detection of intracranial aneurysms (IAs) on magnetic resonance T1 images and test the robustness and performance of framework.
Methods: A retrospective diagnostic study was conducted based on 159 IAs from 136 patients who underwent the T1 images. Among them, 127 cases were randomly selected for training and validation, and 32 cases were used to assess the accuracy and consistency of our algorithm. We developed and assembled three convolutional neural networks for the segmentation and detection of IAs. The segmentation and detection performance of the model were compared with the ground truth, and various metrics were calculated at the voxel level, IAs level, and patient level to show the performance of our framework.
Results: Our assembled model achieved overall Dice, voxel-level sensitivity, specificity, balanced accuracy, and F1 score of 0.802, 0.874, 0.9998, 0.937, and 0.802, respectively. A coincidence greater than 0.7 between the aneurysms predicted by the model and the ground truth was considered as a true positive. For IAs detection, the sensitivity reached 90.63% with 0.58 false positives per case. The volume of IAs segmented by our model showed a high agreement and consistency with the volume of IAs labeled by experts.
Conclusion: The deep learning framework is achievable and robust for IAs segmentation and detection. Our model offers more clinical application opportunities compared to digital subtraction angiography (DSA)-based, CTA-based, and MRA-based methods.
Clinical Relevance Statement: Our deep learning framework effectively detects and segments intracranial aneurysms using clinical routine T1 sequences, showing remarkable effectiveness and offering great potential for improving the detection of latent intracranial aneurysms and enabling early identification.
Key Points: •There is no segmentation method based on clinical routine T1 images. Our study shows that the proper deep learning framework can effectively localize the intracranial aneurysms. •The T1-based segmentation and detection method is more universal than other angiography-based detection methods, which can potentially reduce missed diagnoses caused by the absence of angiography images. •The deep learning framework is robust and has the potential to be applied in a clinical setting.
(© 2023. The Author(s), under exclusive licence to European Society of Radiology.)
Databáze: MEDLINE
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