Chen Cao1,2, Zhiyang Liu3, Guohua Liu3, Song Jin2, Shuang Xia4. 1. Department of Radiology, First Central Clinical College, Tianjin Medical University, Tianjin, China. 2. Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China. 3. Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, College of Electronic Information and Optical Engineering, Nankai University, Tianjin, China. 4. Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
Abstract
BACKGROUND: Gradient-recalled echo (GRE) sequence is time-consuming and not routinely performed. Herein, we aimed to investigate the ability of weakly supervised learning to identify acute ischemic stroke (AIS) and concurrent hemorrhagic infarction based on diffusion-weighted imaging (DWI). METHODS: First, we proposed spatially locating small stroke lesions in different positions and hemorrhagic infarction lesions by residual neural and visual geometry group networks using weakly supervised learning. Next, we compared the sensitivity and specificity for identifying automatically concurrent hemorrhagic infarction in stroke patients with the sensitivity and specificity of human readings of diffusion and b0 images to evaluate the performance of the weakly supervised methods. Also, the labeling time of the weakly supervised approach was compared with that of the fully supervised approach. RESULTS: Data from a total of 1,027 patients were analyzed. The residual neural network displayed a higher sensitivity than did the visual geometry group network in spatially locating the small stroke and hemorrhagic infarction lesions. The residual neural network had significantly greater patient-level sensitivity than did the human readers (98.4% versus 86.2%, P=0.008) in identifying concurrent hemorrhagic infarction with GRE as the reference standard; however, their specificities were comparable (95.4% versus 96.9%, P>0.99). Weak labeling of lesions required significantly less time than did full labeling of lesions (2.667 versus 10.115 minutes, P<0.001). CONCLUSIONS: Weakly supervised learning was able to spatially locate small stroke lesions in different positions and showed more sensitivity than did human reading in identifying concurrent hemorrhagic infarction based on DWI. The proposed approach can reduce the labeling workload. 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.
BACKGROUND: Gradient-recalled echo (GRE) sequence is time-consuming and not routinely performed. Herein, we aimed to investigate the ability of weakly supervised learning to identify acute ischemic stroke (AIS) and concurrent hemorrhagic infarction based on diffusion-weighted imaging (DWI). METHODS: First, we proposed spatially locating small stroke lesions in different positions and hemorrhagic infarction lesions by residual neural and visual geometry group networks using weakly supervised learning. Next, we compared the sensitivity and specificity for identifying automatically concurrent hemorrhagic infarction in stroke patients with the sensitivity and specificity of human readings of diffusion and b0 images to evaluate the performance of the weakly supervised methods. Also, the labeling time of the weakly supervised approach was compared with that of the fully supervised approach. RESULTS: Data from a total of 1,027 patients were analyzed. The residual neural network displayed a higher sensitivity than did the visual geometry group network in spatially locating the small stroke and hemorrhagic infarction lesions. The residual neural network had significantly greater patient-level sensitivity than did the human readers (98.4% versus 86.2%, P=0.008) in identifying concurrent hemorrhagic infarction with GRE as the reference standard; however, their specificities were comparable (95.4% versus 96.9%, P>0.99). Weak labeling of lesions required significantly less time than did full labeling of lesions (2.667 versus 10.115 minutes, P<0.001). CONCLUSIONS: Weakly supervised learning was able to spatially locate small stroke lesions in different positions and showed more sensitivity than did human reading in identifying concurrent hemorrhagic infarction based on DWI. The proposed approach can reduce the labeling workload. 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Entities:
Keywords:
Artificial intelligence; hemorrhage; magnetic resonance imaging; stroke
Authors: Wu Qiu; Hulin Kuang; Ericka Teleg; Johanna M Ospel; Sung Il Sohn; Mohammed Almekhlafi; Mayank Goyal; Michael D Hill; Andrew M Demchuk; Bijoy K Menon Journal: Radiology Date: 2020-01-28 Impact factor: 11.105
Authors: Rongzhao Zhang; Lei Zhao; Wutao Lou; Jill M Abrigo; Vincent C T Mok; Winnie C W Chu; Defeng Wang; Lin Shi Journal: IEEE Trans Med Imaging Date: 2018-03-30 Impact factor: 10.048
Authors: Bin Zhao; Zhiyang Liu; Guohua Liu; Chen Cao; Song Jin; Hong Wu; Shuxue Ding Journal: Comput Math Methods Med Date: 2021-01-29 Impact factor: 2.238