ABSTRACT
Objective Hippocampal atrophy is a clinically important marker for the diagnosis of many psychiatric disorders such as Alzheimer’s disease‚ so accurate segmentation of the hippocampus is an important scientific issue. With the development of deep learning‚ a large number of advanced automatic segmentation method have been proposed. However‚ 3D hippocampal segmentation is still challenging due to the effects of various noises in MRI and unclear boundaries between various classes of the hippocampus. Therefore‚ the aim of this paper is to propose new method to segment the hippocampal head‚ body‚ and tail more accurately. Methods To overcome these challenges‚ this paper proposed two strategies. One was the spatial and frequency domain features adaptive fusion strategy‚ which reduced the influence of noise on feature extraction by automatically selecting the appropriate frequency combination through fast Fourier transform and convolution. The other was an inter-class boundary region enhancement strategy‚ which allowed the network to focus on learning the boundary regions by weighting the loss function of the boundary regions between each class to achieve the goal of pinpointing the boundaries and regulating the size of the hippocampal head‚ body and tail. Results Experiments performed on a 50-case teenager brain MRI dataset show that our method achieves state-of-the-art hippocampal segmentation. Hippocampal head‚ body and tail had been improved compared to the existing method. Ablation experiments demonstrated the effectiveness of our two proposed strategies‚ and we also validated that the network had a strong generalization ability on a 260-case Task04_Hippocampus dataset. It was shown that the method proposed in this paper could be used in more hippocampal segmentation scenarios. Conclusion The method proposed in this paper can help clinicians to observe hippocampal atrophy more clearly and accomplish more accurate diagnosis and follow-up of the condition.
ABSTRACT
Objective To explore the effects of cases-based flipped class in the hybrid teaching for neuroanatomy. Methods A comparative study was conducted among 102 students majored in clinical medicine of 2019 grade, one was the experiment group (n = 51) and another was the control group (n = 51). The traditional teaching method was applied in control group, while the teaching during neuroanatomy based on cases-based flipped class was applied in experimental group. The teaching effects were evaluated by theory and experiment examination and investigated by the questionnaire of students' satisfaction with the new teaching mode. Results Most students supported the cases-based flipped class teaching and thought it was helpful to improve the autonomous learning ability. The satisfaction rate of experimental group on the cases-based flipped class teaching effectiveness was 98.04%. The total scores, the scores of neuroanatomy and the student number who got 81-90 and 91-100 of experiment group were significantly higher than those of control group, and the difference was statistically significant (P<0.05). Conclusion Cases-based flipped class could effectively improve the quality of neuroanatomy teaching and students' learning ability and effects for undergraduates.