Topological SLAM for Colonoscopy Mapping: Leveraging Deep Features and Topological Priors

Основні поняття

ColonSLAM, a topological SLAM system, combines classical metric SLAM with deep features and topological priors to create comprehensive maps of the entire colon during colonoscopy procedures.

Анотація

The paper introduces ColonSLAM, a topological SLAM system that aims to create detailed maps of the entire colon during colonoscopy procedures. The key insights are:

1. Metric SLAM approaches struggle in the colonoscopy domain due to challenges like illumination changes, deformations, and dynamic elements. This results in small, disconnected 3D submaps that cannot be merged.

2. ColonSLAM builds on top of a recent metric SLAM system (CudaSIFT-SLAM) that generates these small submaps. It then continuously finds relationships between distant submaps using a novel deep localization network, transformer-based matching techniques, and topological connectivity priors.

3. The localization network is trained to predict if two images come from the same place, allowing ColonSLAM to identify when submaps represent the same colon location and merge them into a single node in the topological map.

4. Topological priors based on the linear nature of colonoscopy procedures (e.g., the camera cannot jump from the cecum to the transverse colon without passing through the ascending colon) guide the search for covisible submaps.

5. Experiments on the Endomapper dataset show that ColonSLAM can build rich topological maps of the entire colon, outperforming previous approaches in terms of precision and recall. The combination of the localization network, topological priors, and LightGlue matching achieves 90% precision and 70% recall.

6. The resulting topological maps can serve as personalized patient maps, paving the way for assisted navigation and disease monitoring in colonoscopy.

Статистика

CudaSIFT-SLAM is able to build small, disconnected 3D submaps during colonoscopy procedures.
ColonSLAM is able to build complex topological maps with dozens of correctly localized submaps, capturing the full complexity of the colonoscopy exploration.

Цитати

"ColonSLAM is able to reliably build a graph by finding traversability and covisibility connections between distant nodes."
"The graphs obtained with ColonSLAM will serve as personalized patient maps, paving the way to assisted navigation and disease monitoring in colonoscopy."

Ключові висновки, отримані з

Topological SLAM in colonoscopies leveraging deep features and topological priors

by Javi... о arxiv.org 09-26-2024

https://arxiv.org/pdf/2409.16806.pdf

Глибші Запити

How could the long-term relationships between entry and withdrawal phases, as well as relationships between multiple explorations of the same patient, be further leveraged to improve the topological maps?

To enhance the topological maps generated by ColonSLAM, leveraging long-term relationships between entry and withdrawal phases, as well as multiple explorations of the same patient, can be pivotal. By establishing connections between these phases, the system can create a more comprehensive representation of the colon's anatomy and the procedural journey. This can be achieved through the following strategies:

Temporal Mapping: Implementing a temporal mapping framework that records the sequence of entry and withdrawal phases can help in identifying recurring patterns and anatomical landmarks. By associating specific submaps from the entry phase with their corresponding withdrawal phase, the system can create a more cohesive topological graph that reflects the entire exploration process.

Patient-Specific Mapping: Each patient's unique anatomy and history can be integrated into the mapping process. By storing and analyzing data from previous colonoscopies, ColonSLAM can recognize familiar patterns and anatomical structures, allowing for more accurate localization and mapping during subsequent procedures. This could involve using machine learning techniques to predict the likelihood of encountering specific regions based on past explorations.

Enhanced Covisibility Detection: By utilizing advanced visual place recognition techniques, the system can improve its ability to detect covisible submaps across different exploration sessions. This could involve training the localization network to recognize not only immediate covisible areas but also those that may have been observed in previous sessions, thus enriching the topological graph with historical data.

Integration of Clinical Insights: Incorporating clinical insights, such as findings from previous colonoscopies or known disease patterns, can guide the mapping process. For instance, if a patient has a history of polyps in a specific region, the system can prioritize mapping and exploring that area more thoroughly during the current procedure.

By implementing these strategies, ColonSLAM can create a more robust and informative topological map that not only reflects the current exploration but also builds upon the patient's medical history and previous procedures.

What other medical domains beyond colonoscopy could benefit from the topological SLAM approach presented in this work?

The topological SLAM approach presented in ColonSLAM has the potential to be applied in various medical domains beyond colonoscopy. Some of these domains include:

Endoscopy in Other Organs: Similar to colonoscopy, endoscopic procedures in other organs, such as the stomach (gastroscopy) or the respiratory tract (bronchoscopy), could benefit from topological SLAM. The ability to create detailed topological maps of these areas can enhance navigation and visualization during procedures, improving diagnostic accuracy and treatment planning.

Surgical Navigation: In minimally invasive surgeries, topological SLAM can assist surgeons in navigating complex anatomical structures. By providing real-time mapping and localization, the system can help in identifying critical landmarks and avoiding complications, ultimately leading to better surgical outcomes.

Robotic Surgery: The integration of topological SLAM in robotic surgical systems can enhance the precision and efficiency of robotic-assisted procedures. By continuously updating the topological map during surgery, the system can adapt to changes in the surgical field, improving the surgeon's ability to navigate and manipulate instruments.

Radiology and Imaging: In radiology, topological SLAM can be used to correlate imaging data from different modalities (e.g., CT, MRI) with anatomical structures. This can facilitate better localization of lesions or abnormalities, aiding in diagnosis and treatment planning.

Pathology: In pathology, topological SLAM could assist in mapping tissue samples and correlating them with imaging data. This could enhance the understanding of disease progression and improve the accuracy of diagnoses based on histological examinations.

By extending the principles of topological SLAM to these medical domains, healthcare professionals can leverage advanced mapping techniques to improve patient care, enhance procedural accuracy, and facilitate better clinical decision-making.

How could the topological maps generated by ColonSLAM be integrated with other medical data, such as disease annotations or patient history, to enhance diagnostic and treatment capabilities?

Integrating the topological maps generated by ColonSLAM with other medical data, such as disease annotations and patient history, can significantly enhance diagnostic and treatment capabilities. This integration can be achieved through several approaches:

Data Fusion: By combining topological maps with electronic health records (EHRs), clinicians can access a comprehensive view of a patient's medical history alongside the anatomical mapping of the colon. This can include previous diagnoses, treatment plans, and outcomes, allowing for more informed decision-making during procedures.

Disease Annotation Overlay: Disease annotations, such as the presence of polyps, tumors, or inflammatory regions, can be overlaid onto the topological maps. This visual representation can help clinicians quickly identify areas of concern and prioritize their examination during colonoscopy, leading to more efficient and targeted interventions.

Predictive Analytics: Machine learning algorithms can be employed to analyze historical data in conjunction with the topological maps. By identifying patterns and correlations between anatomical features and disease outcomes, the system can provide predictive insights, helping clinicians anticipate potential complications or disease progression.

Personalized Treatment Plans: The integration of topological maps with patient history can facilitate the development of personalized treatment plans. For instance, if a patient has a history of recurrent polyps in specific regions, the system can recommend more frequent surveillance or targeted therapies for those areas.

Real-Time Decision Support: During colonoscopy, real-time access to integrated data can provide decision support for clinicians. For example, if a suspicious lesion is detected, the system can quickly reference the patient's history and previous findings, aiding in the assessment of the lesion's significance and guiding further action.

Longitudinal Tracking: By maintaining a database of topological maps and associated medical data over time, clinicians can track changes in a patient's anatomy and disease status. This longitudinal perspective can enhance monitoring and follow-up care, ensuring timely interventions when necessary.

Through these integration strategies, the topological maps generated by ColonSLAM can serve as a powerful tool in the clinical workflow, enhancing diagnostic accuracy, improving treatment planning, and ultimately leading to better patient outcomes.