Intracranial steno-occlusive lesion detection on time-of-flight MR angiography using multi-task learning

Arenillas, 2011, Intracranial atherosclerosis: current concepts, Stroke, 42, S20, 10.1161/STROKEAHA.110.597278 Au, 2018 Bash, 2005, Intracranial vascular stenosis and occlusive disease: evaluation with CT angiography, MR angiography, and digital subtraction angiography, AJNR. Am. J. Neuroradiol., 26, 1012 Chakraborty, 2004, Observer studies involving detection and localization: modeling, analysis, and validation, Med. Phys., 31, 2313, 10.1118/1.1769352 Chen, 2017, 3D intracranial artery segmentation using a convolutional autoencoder, 714 Chen, 2020, Automated intracranial artery labeling using a graph neural network and hierarchical refinement, 76 Chen, 2021, Deep open snake tracker for vessel tracing, 579 Chen, 2018, Development of a quantitative intracranial vascular features extraction tool on 3D MRA using semiautomated open-curve active contour vessel tracing, Magn. Reson. Med., 79, 3229, 10.1002/mrm.26961 Chen, 2016 Choi, 2007, Detection of intracranial atherosclerotic steno-occlusive disease with 3D time-of-flight magnetic resonance angiography with sensitivity encoding at 3T, Am. J. Neuroradiol., 28, 439 Chung, 2020, Stenosis detection from time-of-flight magnetic resonance angiography via deep learning 3D squeeze and excitation residual networks, IEEE Access, 8, 43325, 10.1109/ACCESS.2020.2977669 Çiçek, 2016, 3D U-Net: Learning dense volumetric segmentation from sparse annotation, 424 Feldmann, 2007, The stroke outcomes and neuroimaging of intracranial atherosclerosis (SONIA) trial, Neurology, 68, 2099, 10.1212/01.wnl.0000261488.05906.c1 Gao, 2020, A feature transfer enabled multi-task deep learning model on medical imaging, Expert Syst. Appl., 143 Han, 2020 He, 2020, Multi-task learning for the segmentation of organs at risk with label dependence, Med. Image Anal., 61 Hou, 2020, 1D CNN-based intracranial aneurysms detection in 3D TOF-MRA, Complex, 2020, 7023754:1, 10.1155/2020/7023754 Jang, 2020, Deep learning-based automatic detection algorithm for reducing overlooked lung cancers on chest radiographs, Radiology, 296, 652, 10.1148/radiol.2020200165 Kamnitsas, 2016, Deepmedic for brain tumor segmentation, 138 Kamnitsas, 2017, Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation, Med. Image Anal., 36, 61, 10.1016/j.media.2016.10.004 Kendall, 2018, Multi-task learning using uncertainty to weigh losses for scene geometry and semantics, 7482 Lee, 2021, Spider U-Net: Incorporating inter-slice connectivity using LSTM for 3D blood vessel segmentation, Appl. Sci., 11 Li, 2015, Conventional T2-weighted imaging to detect high-grade stenosis and occlusion of internal carotid artery, vertebral artery, and basilar Artery, J. Stroke Cerebrovasc. Dis.: Off. J. Natl. Stroke Assoc., 24, 1591, 10.1016/j.jstrokecerebrovasdis.2015.03.028 Lin, 2020, Focal Loss for dense object detection, IEEE Trans. Pattern Anal. Mach. Intell., 42, 318, 10.1109/TPAMI.2018.2858826 Nakao, 2018, Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography, J. Magn. Reson. Imaging, 47, 948, 10.1002/jmri.25842 Nam, 2019, Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs, Radiology, 290, 218, 10.1148/radiol.2018180237 Redmon, J., Divvala, S., Girshick, R., Farhadi, A., 2016. You Only Look Once: Unified, real-Time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 779–788. Redmon, J., Farhadi, A., 2017. YOLO9000: Better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6517–6525. Ronneberger, 2015, U-Net: Convolutional networks for biomedical image segmentation, 234 Ruder, 2017 Sacco, 1995, Race-ethnicity and determinants of intracranial atherosclerotic cerebral infarction. The Northern Manhattan Stroke Study, Stroke, 26, 14, 10.1161/01.STR.26.1.14 Selvaraju, 2019, Grad-CAM: Visual explanations from deep networks via gradient-based localization, Int. J. Comput. Vis., 128, 336, 10.1007/s11263-019-01228-7 Shi, 2015, Convolutional LSTM network: A machine learning approach for precipitation nowcasting, 802 Sichtermann, 2019, Deep learning–based detection of intracranial aneurysms in 3D TOF-MRA, Am. J. Neuroradiol., 40, 25, 10.3174/ajnr.A5911 Stember, 2019, Convolutional neural networks for the detection and measurement of cerebral aneurysms on magnetic resonance angiography, J. Digit. Imaging, 32, 808, 10.1007/s10278-018-0162-z Wang, 2018, Focal dice loss and image dilation for brain tumor segmentation, 119 Wang, 2020, JointVesselNet: Joint volume-projection convolutional embedding networks for 3D cerebrovascular segmentation, 106 Wityk, 1996, Race and sex differences in the distribution of cerebral atherosclerosis, Stroke, 27, 1974, 10.1161/01.STR.27.11.1974 Xia, 2021, A nested parallel multiscale convolution for cerebrovascular segmentation, Med. Phys., 48, 7971, 10.1002/mp.15280 You, 2021, Development and validation of visual grading system for stenosis in intracranial atherosclerotic disease on time-of-flight magnetic resonance angiography, Eur. Radiol., 32, 2781, 10.1007/s00330-021-08319-5 Zhou, 2021 Zreik, 2019, A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography, IEEE Trans. Med. Imaging, 38, 1588, 10.1109/TMI.2018.2883807