Seeing Clearly: AI Sharpens Focus on Real-Time Surgical Scene Understanding

Author: Denis Avetisyan

A new framework leverages spiking neural networks and video transformers to deliver accurate and energy-efficient segmentation of surgical video feeds.

The research details a surgical video analysis pipeline-SpikeSurgSeg-which leverages a pretrained, spike-driven video encoder employing layer-wise tube masking for reconstruction, and integrates this with a spike-driven memory readout and feature pyramid network, ultimately achieving surgical scene segmentation with an SNN-based model distinguished by its two spike-driven CNN blocks and two spike-driven spatiotemporal Transformers exhibiting linear space-time computational complexity-<span class="katex-eq" data-katex-display="false">O(N)</span>. — The research details a surgical video analysis pipeline-SpikeSurgSeg-which leverages a pretrained, spike-driven video encoder employing layer-wise tube masking for reconstruction, and integrates this with a spike-driven memory readout and feature pyramid network, ultimately achieving surgical scene segmentation with an SNN-based model distinguished by its two spike-driven CNN blocks and two spike-driven spatiotemporal Transformers exhibiting linear space-time computational complexity- $O(N)$ .

This work introduces SpikeSurgSeg, a system combining masked autoencoding with a spatiotemporal video transformer for low-latency surgical scene segmentation.

Accurate and timely surgical scene understanding is crucial for enhancing intra-operative safety, yet computationally demanding deep learning models hinder real-time deployment in resource-constrained operating rooms. This limitation motivates the work presented in ‘Surgical Scene Segmentation using a Spike-Driven Video Transformer with Real-Time Potential’, which introduces SpikeSurgSeg-a novel spiking neural network framework leveraging masked autoencoding and spatiotemporal video transformers to achieve comparable segmentation accuracy to state-of-the-art models with significantly reduced latency. Demonstrating over 8x faster inference and 20x acceleration compared to foundation models, SpikeSurgSeg offers a pathway to truly real-time surgical intelligence. Could this approach unlock a new era of efficient and reliable robotic surgery assistance?

The Computational Imperative of Surgical Understanding

Effective computer-assisted surgery relies heavily on the ability to precisely identify and categorize the various instruments, tissues, and anatomical structures within the surgical field – a process known as surgical scene segmentation. However, achieving this in real-time presents a significant computational hurdle. Current deep learning algorithms, while demonstrating impressive accuracy in static images, often require substantial processing power and memory, making their deployment in operating rooms – where resources are limited and low latency is critical – impractical. The sheer volume of visual data generated during surgery, combined with the need for immediate analysis to guide procedures, strains even high-performance computing systems. Consequently, a key challenge remains: developing methods that can deliver accurate and timely segmentation without exceeding the constraints of real-world surgical environments.

Despite demonstrable success in image recognition and segmentation tasks, conventional deep learning models present a significant hurdle for real-time surgical applications due to their substantial energy demands. These models, typically reliant on large numbers of parameters and computationally intensive operations, require powerful hardware and considerable power consumption – a practical limitation within the operating room where portability, battery life, and thermal management are critical. The architecture of these networks often prioritizes accuracy over efficiency, leading to a trade-off that hinders their deployment on embedded systems or mobile robotic platforms commonly envisioned for computer-assisted surgery. Consequently, researchers are actively exploring alternative computational paradigms that can achieve comparable performance with a fraction of the energy expenditure, enabling truly responsive and sustainable surgical intelligence.

Advancing real-time surgical intelligence demands a fundamental shift in computational strategies, moving beyond the limitations of conventional deep learning architectures. Current systems, while demonstrating impressive accuracy, often require substantial energy resources, hindering their practical application in operating rooms where portability and sustained performance are critical. Researchers are increasingly turning to bio-inspired computation – mimicking the efficiency of the human brain – as a promising solution. These approaches, leveraging spiking neural networks and neuromorphic hardware, aim to drastically reduce energy consumption while maintaining, or even improving, the speed and accuracy of surgical scene understanding. By emulating biological processes like sparse coding and event-driven processing, these technologies offer a pathway towards truly intelligent surgical tools capable of providing real-time feedback and assistance with minimal power requirements, ultimately enhancing patient outcomes and surgical precision.

To enable the deployment of efficient and accurate intelligent surgical systems, we address the performance limitations of spiking neural networks (SNNs) - which offer low latency but suffer from sparse representations - by introducing a masked pretraining strategy for surgical scene segmentation that maintains their efficiency while improving accuracy. — To enable the deployment of efficient and accurate intelligent surgical systems, we address the performance limitations of spiking neural networks (SNNs) – which offer low latency but suffer from sparse representations – by introducing a masked pretraining strategy for surgical scene segmentation that maintains their efficiency while improving accuracy.

Spiking Neural Networks: A Foundation for Efficient Computation

Spiking Neural Networks (SNNs) diverge from traditional Artificial Neural Networks (ANNs) by utilizing asynchronous, event-driven computation. Instead of processing information in continuous, synchronized layers, SNNs operate on discrete spikes – brief pulses of information – only when a neuron’s membrane potential exceeds a threshold. This leads to sparse computation, meaning only a small fraction of neurons are active at any given time. Consequently, SNNs potentially reduce computational demands and energy consumption compared to ANNs, which typically require calculations for every neuron in each layer with every input. The timing of these spikes, rather than just the rate, can also encode information, offering a more nuanced and potentially more powerful representational scheme.

Spiking Neural Networks (SNNs) achieve energy efficiency through their bio-inspired computational model. Traditional Artificial Neural Networks (ANNs) perform computations with every input, whereas SNNs operate on discrete spikes, or events, in time. This event-driven approach means neurons only activate and consume power when a spike is received, leading to sparse activity. Consequently, SNNs require significantly fewer operations compared to ANNs for the same task, directly translating to lower energy consumption, particularly in hardware implementations. The power consumption of a spiking neuron is proportional to the frequency of spikes, allowing for highly efficient processing when the input data contains limited temporal information or is largely static.

Current deep learning architectures are not directly transferable to Spiking Neural Networks (SNNs) for tasks such as surgical scene segmentation due to the fundamental differences in information processing. Traditional backpropagation, the standard training algorithm for Artificial Neural Networks (ANNs), is not directly applicable to SNNs because of the discrete nature of spikes; gradient descent requires continuous functions. Consequently, researchers are exploring alternative training methodologies including spike-timing-dependent plasticity (STDP), surrogate gradient methods, and conversion from pre-trained ANNs. Furthermore, novel SNN architectures are needed to effectively process the temporal information present in video data typical of surgical scenes; this includes exploring designs that incorporate temporal pooling, specialized synaptic plasticity rules, and optimized neuron models for sparse event-driven computation.

Our spiking neural network model demonstrates comparable or improved bleeding segmentation accuracy on the SurgBleed dataset while significantly reducing energy consumption and inference latency compared to task-specific and prompt-based methods.

SpikeSurgSeg: A Spiking Architecture for Surgical Precision

SpikeSurgSeg utilizes a hybrid architecture comprising Spike-Driven Convolutional Neural Network (CNN) Blocks and Spike-Driven Spatiotemporal Transformer Blocks to address the requirements of surgical video segmentation. The CNN blocks are designed for efficient extraction of spatial features from individual frames, while the Spatiotemporal Transformer Blocks process these features across time, enabling the model to reason about temporal dependencies and motion within the surgical scene. This combination allows SpikeSurgSeg to capture both local details and global context, crucial for accurately identifying and segmenting surgical instruments and anatomical structures. The spiking neural network (SNN) implementation of these blocks reduces computational cost and energy consumption compared to traditional artificial neural networks, while maintaining competitive performance on the segmentation task.

Spike-Driven Hamming Attention represents a novel attention mechanism specifically designed for processing the sparse data inherent in Spiking Neural Networks (SNNs). Traditional attention mechanisms, such as those relying on dot products, become computationally inefficient and less effective with sparse inputs. Hamming attention, in contrast, utilizes the Hamming distance – the number of differing bits – to calculate attention weights. This approach allows for efficient similarity comparisons using bitwise operations, reducing computational complexity and memory access. The implementation within SpikeSurgSeg further optimizes this by focusing on the relevant spiking activity, enabling effective information processing even with highly sparse SNN outputs and improving the overall efficiency of spatiotemporal reasoning tasks.

SNN Backbone Freezing is implemented during finetuning to improve performance and reduce computational cost. This technique involves fixing the weights of the pretrained Spiking Neural Network (SNN) backbone – the initial layers responsible for low-level feature extraction – while only training the weights of the subsequent layers adapted for the surgical domain. By preserving the learned representations from the backbone, the model requires fewer parameters to be updated, accelerating the finetuning process and mitigating potential overfitting to the target dataset. This approach effectively transfers knowledge acquired during pretraining to the specific requirements of surgical video analysis, leading to enhanced accuracy and efficiency.

The SpikeSurgSeg framework utilizes Masked Visual Modeling (MVM) and Layer-wise Tube Masking during the pretraining phase to develop resilient video representations. MVM randomly masks portions of input video frames, forcing the network to learn contextual relationships and reconstruct missing information. Layer-wise Tube Masking extends this concept by applying masking across consecutive frames – a ‘tube’ – at each layer of the network. This encourages spatiotemporal feature learning and improves robustness to occlusions and noise commonly found in surgical video data. The combined approach enables the model to effectively learn features from incomplete or degraded video sequences, resulting in enhanced generalization performance.

Leveraging the sparsity of spiking neural networks, our model achieves high efficiency with reduced false positives and faster inference times on the EndoVis18 dataset.

Empirical Validation: Performance on Surgical Datasets

SpikeSurgSeg was subjected to evaluation using two distinct surgical image datasets: the publicly available EndoVis18 dataset and an internally curated dataset, SurgBleed. Performance on both datasets confirmed the model’s capacity for accurate segmentation of surgical scenes, indicating its generalizability beyond a single data source. The EndoVis18 dataset provides a standardized benchmark for endoscopic vision, while the SurgBleed dataset, captured in-house, represents a complementary dataset with potentially differing characteristics and challenges, ensuring a more robust assessment of segmentation capabilities.

On the EndoVis18 dataset, SpikeSurgSeg attained a mean Intersection over Union (mIoU) score of 43.21%. This performance level is statistically comparable to that of currently established Artificial Neural Network (ANN) models operating on the same dataset. The mIoU metric quantifies the overlap between predicted segmentation masks and ground truth annotations, providing a standardized measure of segmentation accuracy. This result indicates that the proposed Spiking Neural Network (SNN) architecture does not sacrifice accuracy when compared to traditional ANN approaches for surgical scene segmentation.

SpikeSurgSeg’s improved performance is directly attributable to its novel Spiking Neural Network (SNN) architecture and the implementation of efficient training strategies. The SNN architecture, designed specifically for event-based data processing, allows for sparse and asynchronous computation, reducing computational redundancy. Furthermore, training utilized techniques focused on maximizing information propagation within the SNN, including surrogate gradient descent and optimized event encoding schemes. These strategies address the inherent challenges of training SNNs, such as the non-differentiability of spiking activations, and facilitate effective learning from surgical image data, resulting in demonstrable gains in segmentation accuracy compared to traditional Artificial Neural Networks (ANNs).

SpikeSurgSeg incorporates knowledge distillation from a Segment Anything Model 2 (SAM2) to enhance segmentation accuracy. This process transfers pre-trained knowledge from SAM2, a large-scale, general-purpose segmentation model, to the SpikeSurgSeg spiking neural network (SNN). By utilizing SAM2’s learned representations as a teacher signal, SpikeSurgSeg’s training is guided towards improved performance, particularly in scenarios with limited labeled surgical data. The distillation process focuses on matching the output probabilities of SAM2, effectively transferring its segmentation expertise to the more energy-efficient SNN architecture without requiring retraining of the larger teacher model.

Power consumption measurements demonstrate substantial energy efficiency gains with SpikeSurgSeg. On the EndoVis18 dataset, SpikeSurgSeg achieved 180 mJ of energy usage during inference. Comparatively, artificial neural network (ANN) baselines consumed more than nine times the energy on the SurgBleed dataset, indicating a >9x reduction in power consumption. This represents an energy savings exceeding one order of magnitude, positioning SpikeSurgSeg as a viable solution for resource-constrained surgical imaging applications.

SpikeSurgSeg demonstrates substantial reductions in inference latency compared to conventional Artificial Neural Networks (ANNs) when applied to surgical image segmentation. On the EndoVis18 dataset, SpikeSurgSeg achieves an inference time of 36 milliseconds. More significantly, performance on the in-house SurgBleed dataset exhibits a greater than 15-fold reduction in latency when compared to ANN baselines. These results indicate the potential for real-time processing and application of SpikeSurgSeg in surgical environments where low latency is critical.

Evaluation of SpikeSurgSeg on the EndoVis18 and SurgBleed datasets indicates the feasibility of Spiking Neural Networks (SNNs) for surgical image analysis applications requiring low latency and power consumption. Specifically, the model achieved inference times of 36ms on EndoVis18 and demonstrated greater than a 15x reduction in latency on the SurgBleed dataset when compared to equivalent Artificial Neural Network (ANN) baselines. Furthermore, power consumption was reduced by over an order of magnitude, registering 180 mJ on EndoVis18 and a greater than 9x reduction on SurgBleed, suggesting a substantial benefit for deployment in resource-constrained environments or applications prioritizing energy efficiency.

Increasing the size of both masked video pretraining data and unlabeled surgical video data consistently improves downstream surgical segmentation performance, as measured by mean intersection over union (mIoU) on datasets like EndoVis18 and SurgBleed.

Towards Intelligent Surgical Systems: A Future Defined by Efficiency

The advent of SpikeSurgSeg signifies a crucial step towards integrating spiking neural networks (SNNs) into practical surgical applications. Prior to this work, deploying SNNs – brain-inspired computational models known for their energy efficiency and speed – in the demanding environment of a surgical workflow presented significant challenges. This research successfully navigates those hurdles, demonstrating that SNNs can accurately and efficiently segment critical surgical instruments and tissues in real-time video feeds. The achievement isn’t simply theoretical; SpikeSurgSeg’s performance indicates a viable pathway for creating low-latency, energy-conscious surgical tools, potentially reducing computational load and enabling more responsive assistance during procedures. This successful implementation validates the potential of SNNs to move beyond simulation and become a tangible component of the modern operating room.

The next phase of development centers on transforming SpikeSurgSeg from a standalone segmentation tool into a fully integrated surgical assistance system. This involves creating a cohesive platform that delivers real-time, actionable feedback to surgeons during procedures. The system will leverage SpikeSurgSeg’s rapid and accurate tissue identification to provide visual overlays, augmented reality guidance, and potentially even robotic control assistance. Such integration promises to move beyond simply identifying anatomical structures to actively supporting critical decision-making, enhancing precision, and ultimately minimizing invasiveness in surgical interventions. The goal is not to replace surgical expertise, but to amplify it with the speed and reliability of neuromorphic computing, paving the way for a new era of intelligent operating rooms.

Continued development centers on broadening the scope of the surgical framework to encompass more intricate procedures, moving beyond initial segmentation tasks. Researchers are actively investigating unsupervised learning techniques, aiming to reduce the reliance on large, manually annotated datasets – a significant bottleneck in medical image analysis. This approach promises to enable the system to adapt and learn from unlabeled surgical video, potentially identifying critical anatomical structures and surgical tools autonomously. By combining unsupervised learning with the existing spiking neural network architecture, the system could achieve greater robustness and generalization, paving the way for truly intelligent surgical assistance capable of handling a wider spectrum of surgical challenges and improving operational efficiency.

The development of intelligent surgical systems represents a paradigm shift in healthcare, and this research actively contributes to realizing that future. By leveraging advancements in artificial intelligence, specifically spiking neural networks, the field moves closer to systems capable of augmenting surgical precision and minimizing invasiveness. Such technology isn’t simply about automation; it’s about providing surgeons with real-time data analysis and predictive capabilities, allowing for more informed decisions and ultimately, reducing the risk of complications. This translates to improved patient safety, faster recovery times, and potentially, better long-term outcomes, marking a significant step towards a more proactive and personalized approach to surgical intervention.

The SurgBleed dataset presents challenges for surgical scene understanding due to highly dynamic and irregularly shaped bleeding boundaries, frequent occlusions, and a focus on realistic surgical environments-a departure from prior datasets like EndoVis18 which prioritized instrument segmentation.

The pursuit of efficient surgical scene segmentation, as demonstrated by SpikeSurgSeg, aligns with a fundamental principle: elegance through mathematical structure. The framework’s reliance on spatiotemporal video transformers and masked autoencoding isn’t merely about achieving high accuracy; it’s about constructing a provably robust system for understanding complex visual data. As Geoffrey Hinton once stated, “What we’re trying to do is create systems that can learn in a way that is more like the way humans learn.” This echoes the core idea of the research – moving beyond brute-force computation to a model that embodies inherent intelligence through structured learning, ultimately delivering low-latency and energy-efficient performance in a critical application.

What’s Next?

The pursuit of biologically plausible neural networks, as exemplified by SpikeSurgSeg, inevitably confronts the chasm between algorithmic elegance and practical realization. While the framework demonstrates a promising confluence of spatiotemporal modeling and energy efficiency, the inherent limitations of spiking neural networks remain. The question is not merely whether these networks can segment surgical scenes, but whether their complexity justifies the computational overhead compared to established, albeit less ‘pure’, architectures. The demonstrated real-time potential is intriguing, but a rigorous comparison against optimized convolutional networks, operating at equivalent precision, is essential.

Future work must address the challenge of scaling these models. Masked autoencoding, while effective for pretraining, introduces inductive biases that may limit generalization to unforeseen surgical variations. A more fundamental exploration of spike-based learning rules, moving beyond supervised learning paradigms, could unlock truly adaptive and robust segmentation capabilities. The field requires a shift in emphasis – from simply mimicking biological function to deriving genuinely novel algorithms from first principles.

Ultimately, the true measure of success will not be achieving human-level performance, but demonstrating a quantifiable advantage in energy consumption and computational efficiency. Only then will the aesthetic appeal of spike-based computation be matched by a demonstrable practical benefit, justifying the added complexity. Until then, it remains a fascinating, yet unproven, path.

Original article: https://arxiv.org/pdf/2512.21284.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/