Surgical navigation has proven to be an effective approach for providing real-time guidance and visualization of relevant information by estimating the pose of the patient’s anatomy and surgical tools. During navigated surgery, instruments are commonly equipped with fiducial markers and tracked by stationary optical tracking systems (OTS) to provide accurate navigation cues. Augmented Reality (AR) has been adopted for intuitive visual guidance, even motivating several efforts to enable surgical instrument tracking through built-in sensors on Head-Mounted Displays (HMDs). However, existing tracking methods typically require a direct line-of-sight to instruments, which is challenging to maintain in dynamic surgical environments due to frequent occlusions caused by moving medical equipment, surgical tools, and personnel. To address this challenge, this work introduces a novel framework capable of tracking surgical instruments even under occlusion by fusing different sensors in a dynamic scene graph representation. Our framework uniquely combines tracking systems with varying degrees of accuracy, providing real-time assessments of tracking reliability to the user. Unlike conventional sensor fusion approaches that are heavily dependent on specific sensor modalities, the proposed method is agnostic to tracking device modality and robust to their motion states (e.g., stationary OTS versus dynamic AR-HMD). Experimental results demonstrate that our dynamic scene graph framework successfully integrates and optimizes measurements from multiple tracking sources, significantly enhancing AR visualization consistency and accuracy with robustness under occlusion.

The segmentation of pelvic fracture fragments in CT and X-ray images is crucial for trauma diagnosis, surgical planning, and intraoperative guidance. However, accurately and efficiently delineating the bone fragments remains a significant challenge due to complex anatomy and imaging limitations. The PENGWIN challenge, organized as a MICCAI 2024 satellite event, aimed to advance automated fracture segmentation by benchmarking state-of-the-art algorithms on these complex tasks. A diverse dataset of 150 CT scans was collected from multiple clinical centers, and a large set of simulated X-ray images was generated using the DeepDRR method. Final submissions from 16 teams worldwide were evaluated under a rigorous multi-metric testing scheme. The top-performing CT algorithm achieved an average fragment-wise intersection over union (IoU) of 0.930, demonstrating satisfactory accuracy. However, in the X-ray task, the best algorithm achieved an IoU of 0.774, which is promising but not yet sufficient for intra-operative decision-making, reflecting the inherent challenges of fragment overlap in projection imaging. Beyond the quantitative evaluation, the challenge revealed methodological diversity in algorithm design. Variations in instance representation, such as primary-secondary classification versus boundary-core separation, led to differing segmentation strategies. Despite promising results, the challenge also exposed inherent uncertainties in fragment definition, particularly in cases of incomplete fractures. These findings suggest that interactive segmentation approaches, integrating human decision-making with task-relevant information, may be essential for improving model reliability and clinical applicability.

Read mapping, which maps billions of reads to a reference DNA, poses a significant performance bottleneck in genomic analysis. Current accelerators for read mapping are primarily bounded by the intensive and random memory access to huge datasets. Near-data processing (NDP) infrastructures are promising to provide extremely high bandwidth. However, existing frameworks failed to reach this potential due to poor locality and high redundancy. Our idea is to introduce prediction under the insight that candidate mapping positions become predictable when the reference is organized in coarse-grain slices. We present GEM (GenomicMemory), an ultra-efficient near-memory accelerator for read mapping. GEM adopts a novel data-centric framework, named dividing-and-predictive-scattering (DPS), which synthesizes information of seed existence to predict the target mapping locations to reduce memory access redundancy. During preparation, DPS divides the reference into coarse-grained slices and creates predictive filters to assess the likelihood of reads belonging to each slice. During mapping, DPS predicts and scatters reads to considerably fewer slices compared than without prediction. By employing small on-chip SRAM-based predictors with high accuracy, DPS minimizes unnecessary DRAM access and data movement from remote memory. In essence, DPS trades pre-seeding predictors for localized access patterns and low redundancy, hence achieving high throughput for data-intensive applications. We implement GEM by integrating coarse-grain reconfigurable architectures (CGRAs) in the logic layer of a 3D-stacked DRAM infrastructure, utilizing the massive banks as slices. GEM leverages CGRAs for their flexibility in supporting various algorithms tailored to different datasets. Bloom filters are leveraged for slice prediction, providing an error rate below 1%. Evaluation results demonstrate that GEM reduces memory requests by 95% and alignments by 87%, achieving a throughput improvement of 15.3× and 11.0× compared to compute-centric and broadcast-based baselines on the same NDP platform. Overall, GEM achieves a$3.5\times$throughput improvement and$2.1\times$energy efficiency compared to state-of-the-art ASIC accelerators.

Electroencephalography (EEG) decoding is an important part of Visual Evoked Potentials-based Brain-Computer Interfaces (BCIs), which directly determines the performance of BCIs. However, long-time attention to repetitive visual stimuli could cause physical and psychological fatigue, resulting in weaker reliable response and stronger noise interference, which exacerbates the difficulty of Visual Evoked Potentials EEG decoding. In this state, subjects' attention could not be concentrated enough and the frequency response of their brains becomes less reliable. To solve these problems, we propose an attention-based parallel multiscale convolutional neural network (AMS-CNN). Specifically, the AMS-CNN first extract robust temporal representations via two parallel convolutional layers with small and large temporal filters respectively. Then, we employ two sequential convolution blocks for spatial fusion and temporal fusion to extract advanced feature representations. Further, we use attention mechanism to weight the features at different moments according to the output-related interest. Finally, we employ a full connected layer with softmax activation function for classification. Two fatigue datasets collected from our lab are implemented to validate the superior classification performance of the proposed method compared to the state-of-the-art methods. Analysis reveals the competitiveness of multiscale convolution and attention mechanism. These results suggest that the proposed framework is a promising solution to improving the decoding performance of Visual Evoked Potential BCIs.

Brain–computer interface (BCI) systems based on electroencephalography (EEG) signals have been extensively used in medical practice. To enhance the BCI performance, improving the classification accuracy of EEG signals is the key, which has always been the focus of research and development. In this article, a novel method integrating complex network and broad learning system (BLS) is proposed for visual evoked potential (VEP)-based BCI research. First, systematic VEP-based brain experiments are conducted for obtaining EEG signals, including steady-state VEP (SSVEP) and steady-state motion VEP (SSMVEP). Then, limited penetrable visibility graph (LPVG) and its degree sequence are employed to implement the preliminary feature extraction. All these features are finally fed into a BLS to study and classify the SSVEP and SSMVEP signals, respectively. The classification results show that our LPVG-based BLS can effectively classify VEP-based EEG signals, with average classification accuracy 96.22% for SSVEP and 74.54% for SSMVEP. These results are significantly better than other comparison methods as well as traditional CCA-based methods. All these open up new venues for studying EEG-based BCI systems via the fusion of network science and BLS.