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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.
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[Abstract] Objective To track and make statistics of the right pulmonary arteries’ branches and to study the clinical significance. Methods Technologies of vascular volume rending were used to analyze the 350 CT pulmonary angiography images. And the three-dimensional display was used to classify the pulmonary arteries. Results According to the 350 reconstructed result, the right upper pulmonary arteries were divided into 6 types: anterior trunk+ post ascending artery, anterior trunk, anterior trunk+ anterior ascending artery+ post ascending artery, superior anterior trunk + inferior anterior trunk+ post ascending artery, anterior trunk+ anterior ascending artery and superior anterior trunk+ inferior anterior trunk. The right middle pulmonary arteries were divided into 2 types: one branch and two branches. As for the right lower pulmonary arteries, the apical arteries were divided into 3 types: one branch, two branches and three branches. The basal segmental arteries were divided into 3 types: medial basal segmentalartery+anterior basal segmental artery+lateroposterior basal segmental artery, medioanterior basal segmental artery+lateroposterior basal segmental artery and medial basal segmental artery+anterior and lateral basal segmental artery +posterior basal segmental artery. To be specific, anterior trunk+ post ascending artery type (56. 6%) occupied the most in the right upper pulmonary arteries. The majority type of right middle pulmonary artery was one branch(57. 4%). In the right lower pulmonary arteries, apical artery of the lower lobe (one branch)+medial basal segmental artery+anterior basal segmental artery+lateroposterior basal segmental artery type(32. 5%) was most common. Conclusion The classification of the right pulmonary arteries based on the three dimensional reconstruction of CT pulmonary angiography is of significant to surgical precision therapy of lung tumor.
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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.
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Objective To study the anatomical characteristics of sectional clivus region and to provide anatomical data for the operation of the clivus regoin.Methods Continuous thin sections(transverse position and coronal position)from two head specimens were performed with freezing milling technique.Observed the anatomical relationship and anatomical characteristics of the clivus region,and finished the three dimensional reconstruction based on the data of sections.Results Totally obtained 320 transverse sections and 232 coronary sections.The related structures were described in 4 typical sections,and three dimensional reconstruction of the clivus region were produced.Conclusion Combinationof section anatomy and three dimensional reconstruction can stereoscopically display the anatomical characters of the clivus re -gion.The three dimensional models could continuously and dynamically display the anatomic structures.