Three-dimensional (3D) spinal ultrasound imaging has demonstrated its promising potential in measuring spinal deformity through recent studies, and it is more suitable for massive early screening and longitudinal follow-up of adolescent idiopathic scoliosis (AIS) compared with X-ray imaging due to its radiation-free superiority. Moreover, some deformities with low observability, such as vertebral rotation, in X-ray images can also be reflected by critical landmarks in 3D ultrasound data. In this paper, we propose a localization network (LLNet) to extract lamina in 3D ultrasound data, which has been indicated as a meaningful anatomy for measuring vertebral rotation by clinical studies. First, the LLNet skillfully establishes a parallel anatomical prior embedding branch that implicitly explores the anatomical correlation between the lamina and another anatomy with more stable observability (spinous process) during the training phase and then introduces the correlation to highlight the potential region of the lamina in the inferring one. Second, since the lamina is a tiny target, the information loss caused by continuous convolutional and pooling operations has a profound negative effect on detecting the lamina. We employ an optimization mechanism to mitigate this problem, which refines feature maps according to information from the original image and further reuses them to polish output. Furthermore, a modified global-local attention module is deployed on skip connections to mine global dependencies and contextual information to construct an effective image pattern. Extensive comparisons and ablation studies are performed on actual clinical data. Results indicate that the capability of our model is better than other outstanding detection models, and functional modules effectively contribute to this, with a 100.0 % detection success rate and an 8.9 % improvement of mean intersection over the union. Thus, our model is promising to become a helpful part of a computer-assisted diagnosis system based on 3D spinal ultrasound imaging.

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. Computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. For the segmentation task, overall baseline performed was the top performing (aggregated mIoU of 0.6763) and was the best on the vessel class (mIoU of 0.5817) while team RREB was the best on the tool (mIoU of 0.6335) and fetus (mIoU of 0.5178) classes. For the registration task, overall the baseline performed better than team SANO with an overall mean 5-frame SSIM of 0.9348. Qualitatively, it was observed that team SANO performed better in planar scenarios, while baseline was better in non-planner scenarios. The detailed analysis showed that no single team outperformed on all 6 test fetoscopic videos. The challenge provided an opportunity to create generalized solutions for fetoscopic scene understanding and mosaicking. In this paper, we present the findings of the FetReg2021 challenge, alongside reporting a detailed literature review for CAI in TTTS fetoscopy. Through this challenge, its analysis and the release of multi-center fetoscopic data, we provide a benchmark for future research in this field.

Spinal X-ray effectively visualizes the overall spinal situation and vertebral details. However, X-ray is unsuitable for long-term follow-up or frequent monitoring due to its radiation hazard. Motivated by this, we propose a model named dual-decomposition radiograph synthesizer (DDRS) to predict the X-ray image of the present moment, given the previous X-ray image and a pair of three-dimensional spinal ultrasound data, for a practical and radiation-free evaluation of spinal deformity in follow-up or monitoring. The DDRS used a novel dual-decomposition strategy to ensure the quality of synthesized images. First, the DDRS innovatively converted the X-ray image synthesis into a fusion between the spinal pose and anatomical information. A parallel architecture was used to extract and aggregate the two information from ultrasound and X-ray images. Second, an intermodality calibration module and a global-local cooperated feature extractor are further introduced to implement our synthesis strategy effectively. The intermodality calibration module provides an accurate spinal pose description by correcting a potential pose difference during two image acquisition times. The global-local cooperated feature extractor contributes to preserving spinal anatomical information in the previous X-ray image by exploring global dependencies and highlighting local details. Extensive experiments were conducted on a real clinical dataset. Results show that a mean structural similarity (SSIM) of 0.89 was obtained between synthesized X-ray images provided by the DDRS and real ones, and further comparisons with existing outstanding image synthesizers also display a 17.1% improvement in mean SSIM, illustrating the potential of our synthesizer in a radiation-free follow-up of scoliosis.

Multimodal analysis of placental ultrasound (US) and microflow imaging (MFI) could greatly aid in the early diagnosis and interventional treatment of placental insufficiency (PI), ensuring a normal pregnancy. Existing multimodal analysis methods have weaknesses in multimodal feature representation and modal knowledge definitions and fail on incomplete datasets with unpaired multimodal samples. To address these challenges and efficiently leverage the incomplete multimodal dataset for accurate PI diagnosis, we propose a novel graph-based manifold regularization learning (MRL) framework named GMRLNet. It takes US and MFI images as input and exploits their modality-shared and modality-specific information for optimal multimodal feature representation. Specifically, a graph convolutional-based shared and specific transfer network (GSSTN) is designed to explore intra-modal feature associations, thus decoupling each modal input into interpretable shared and specific spaces. For unimodal knowledge definitions, graph-based manifold knowledge is introduced to describe the sample-level feature representation, local inter-sample relations, and global data distribution of each modality. Then, an MRL paradigm is designed for inter-modal manifold knowledge transfer to obtain effective cross-modal feature representations. Furthermore, MRL transfers the knowledge between both paired and unpaired data for robust learning on incomplete datasets. Experiments were conducted on two clinical datasets to validate the PI classification performance and generalization of GMRLNet. State-of-the-art comparisons show the higher accuracy of GMRLNet on incomplete datasets. Our method achieves 0.913 AUC and 0.904 balanced accuracy (bACC) for paired US and MFI images, as well as 0.906 AUC and 0.888 bACC for unimodal US images, illustrating its application potential in PI CAD systems.

With the development of social media, the information about vector-borne disease incidence over broad spatial scales can cause demand for local vector control before local risk exists. Anticipatory intervention may still benefit local disease control efforts; however, infection risks are not the only focal concerns governing public demand for vector control. Concern for environmental contamination from pesticides and economic limitations on the frequency and magnitude of control measures also play key roles. Further, public concern may be focused more on ecological factors (i.e., controlling mosquito populations) or on epidemiological factors (i.e., controlling infection-carrying mosquitoes), which may lead to very different control outcomes. Here we introduced a generic Ross-MacDonald model, incorporating these factors under three spatial scales of disease information: local, regional, and global. We tailored and parameterized the model for Zika virus transmitted by Aedes aegypti mosquito. We found that sensitive reactivity caused by larger-scale incidence information could decrease average human infections per patch breeding capacity, however, the associated increase in total control effort plays a larger role, which leads to an overall decrease in control efficacy. The shift of focal concerns from epidemiological to ecological risk could relax the negative effect of the sensitive reactivity on control efficacy when mosquito breeding capacity populations are expected to be large. This work demonstrates that, depending on expected total mosquito breeding capacity population size, and weights of different focal concerns, large-scale disease information can reduce disease infections without lowering control efficacy. Our findings provide guidance for vector-control strategies by considering public reaction through social media.