Super-resolution landmark detection networks for medical images.

Craniomaxillofacial (CMF) and nasal landmark detection are fundamental components in computer-assisted surgery. Medical landmark detection method includes regression-based and heatmap-based methods, and heatmap-based methods are among the main methodology branches. The method relies on high-resolution (HR) features containing more location information to reduce the network error caused by sub-pixel location. Previous studies extracted HR patches around each landmark from downsampling images via object detection and subsequently input them into the network to obtain HR features. Complex multistage tasks affect accuracy. The network error caused by downsampling and upsampling operations during training, which interpolates low-resolution features to generate HR features or predicted heatmap, is still significant. We propose standard super-resolution landmark detection networks (SRLD-Net) and super-resolution UNet (SR-UNet) to reduce network error effectively. SRLD-Net used Pyramid pooling block, Pyramid fusion block and super-resolution fusion block to combine global prior knowledge and multi-scale local features, similarly, SR-UNet adopts Pyramid pooling block and super-resolution block. They can obviously improve representation learning ability of our proposed methods. Then the super-resolution upsampling layer is utilized to generate detail predicted heatmap. Our proposed networks were compared to state-of-the-art methods using the craniomaxillofacial, nasal, and mandibular molar datasets, demonstrating better performance. The mean errors of 18 CMF, 6 nasal and 14 mandibular landmarks are 1.39 ± 1.04, 1.31 ± 1.09, 2.01 ± 4.33 mm. These results indicate that the super-resolution methods have great potential in medical landmark detection tasks. This paper provides two effective heatmap-based landmark detection networks and the code is released in https://github.com/Runshi-Zhang/SRLD-Net.

[A multi-center observation of the therapeutic efficacy of Bencycloquidium bromide in the treatment of seasonal allergic rhinitis with predominant symptoms of rhinorrhea].

Objective:To observe the efficacy and safety of the M receptor antagonist Bencycloquidium bromide nasal spray in treatment of seasonal allergic rhinitis with runny nose as the main symptom. Methods:From August 2021 to September 2021, 134 patients with seasonal allergic rhinitis were enrolled in the otolaryngology Outpatient Department of Peking University Third Hospital, First Affiliated Hospital of Harbin Medical University and China-Japanese Friendship Hospital of Jilin University, including 71 males and 63 females, with a median age of 38 years. TNSS score and visual analogue scale（VAS） of total nasal symptoms were observed during 2 weeks of treatment with Bencycloquidium bromide nasal spray. Results:TNSS score decreased from （8.89±3.31） on day 0 to （3.71±2.51） on day 14（P<0.001）, VAS score of nasal symptoms decreased from （24.86±7.40） on day 0 to （6.84±5.94） on day 14（P<0.001）, VAS score of rhinorrhoea decreased from （6.88±2.06） on day 0 to （1.91±1.81） on day 14（P<0.001）. Rhinoconjunctivitis quality of life questionnaire（RQLQ） score decreased from （94.63±33.35） on day 0 to （44.95±32.28） on day 14（P<0.001）. The incidence of adverse reaction was low and no serious adverse events occurred during the whole experiment. Conclusion:Bencycloquidium bromide nasal spray has significant efficacy and good safety in the treatment of seasonal allergic rhinitis.