AI-assisted automatic MRI-based tongue volume evaluation in motor neuron disease (MND)
ConclusionUtilizing single U-Net trained on three orthogonal orientations with consequent merging of respective orientations in an optimized consensus model reduces the number of erroneous detections and improves the segmentation of the tongue. The CNN-based automatic segmentation allows for accurate quantification of the tongue volumes in all subjects. The application to the ALS variant PBP showed significant reduction of the tongue volume in these patients and opens the way for unbiased future longitudinal studies in diseases affecting tongue volume. (Source: International Journal of Computer Assisted Radiology and Surgery)
Source: International Journal of Computer Assisted Radiology and Surgery - March 27, 2024 Category: Intensive Care Source Type: research

PitSurgRT: real-time localization of critical anatomical structures in endoscopic pituitary surgery
ConclusionThe results from the quantitative evaluation, real-time acceleration, and neurosurgeon study demonstrate the proposed method is highly promising in providing real-time intraoperative guidance of the critical anatomical structures in endoscopic pituitary surgery. (Source: International Journal of Computer Assisted Radiology and Surgery)
Source: International Journal of Computer Assisted Radiology and Surgery - March 25, 2024 Category: Intensive Care Source Type: research

AIxSuture: vision-based assessment of open suturing skills
ConclusionWe provide a thorough analysis of a new dataset as well as novel benchmarking results for surgical skill assessment. This opens the doors to new advances in skill assessment by enabling video-based skill assessment for classic surgical techniques with the potential to improve the surgical outcome of patients. (Source: International Journal of Computer Assisted Radiology and Surgery)
Source: International Journal of Computer Assisted Radiology and Surgery - March 25, 2024 Category: Intensive Care Source Type: research

Deep learning-based automatic pipeline for 3D needle localization on intra-procedural 3D MRI
ConclusionThe proposed automatic pipeline achieved rapid pixel-level 3D needle localization on intra-procedural 3D MRI without requiring a large 3D training dataset and has the potential to assist MRI-guided percutaneous interventions. (Source: International Journal of Computer Assisted Radiology and Surgery)
Source: International Journal of Computer Assisted Radiology and Surgery - March 23, 2024 Category: Intensive Care Source Type: research

SF-TMN: SlowFast temporal modeling network for surgical phase recognition
ConclusionThe improvement in the results shows that combining temporal information from both frame level and segment level by refining outputs with temporal refinement stages is beneficial for the temporal modeling of surgical phases. (Source: International Journal of Computer Assisted Radiology and Surgery)
Source: International Journal of Computer Assisted Radiology and Surgery - March 21, 2024 Category: Intensive Care Source Type: research

Near-real-time Mueller polarimetric image processing for neurosurgical intervention
Conclusion:The end-to-end image processing achieved real-time performance for a localised field of view (\(\approx 6.5\ \text {mm}^2\)). The denoised polarimetric intensities produced visibly clear directional patterns of neuronal fibre tracts in line with reference polarimetric image quality standards; directional disruption was kept in case of neoplastic lesions. The presented advances pave the way towards feasible oncological neurosurgical translations of novel, label-free, interventional feedback. (Source: International Journal of Computer Assisted Radiology and Surgery)
Source: International Journal of Computer Assisted Radiology and Surgery - March 19, 2024 Category: Intensive Care Source Type: research

Anatomical attention can help to segment the dilated pancreatic duct in abdominal CT
ConclusionsWe proposed an anatomical attention-based strategy for the dilated pancreatic duct segmentation. Our proposed strategy significantly outperforms earlier approaches. The attention mechanism helps to focus on the pancreas region, while the enhancement of the tubular structure enables FCNs to capture the vessel-like structure. The proposed technique might be applied to other tube-like structure segmentation tasks within targeted anatomies. (Source: International Journal of Computer Assisted Radiology and Surgery)
Source: International Journal of Computer Assisted Radiology and Surgery - March 18, 2024 Category: Intensive Care Source Type: research

Model guided medicine and the search for truth
(Source: International Journal of Computer Assisted Radiology and Surgery)
Source: International Journal of Computer Assisted Radiology and Surgery - March 18, 2024 Category: Intensive Care Source Type: research

3D magnetic seed localization for augmented reality in surgery
ConclusionTracking the magnetic detection probe allows 3D localization of a magnetic seed, which opens doors for augmented reality target visualization during surgery. Such an approach should enhance the perception of the localized region of interest during the intervention, especially for breast tumor resection where magnetic seeds can already be used in the protocol. (Source: International Journal of Computer Assisted Radiology and Surgery)
Source: International Journal of Computer Assisted Radiology and Surgery - March 16, 2024 Category: Intensive Care Source Type: research

PELE scores: pelvic X-ray landmark detection with pelvis extraction and enhancement
ConclusionThe design of PELE module can improve the accuracy of different pelvic landmark detection baselines, which we believe is obviously conducive to the positioning and inspection of clinical landmarks and critical structures, thus better serving downstream tasks. Our project has been open-sourced athttps://github.com/ECNUACRush/PELEscores. (Source: International Journal of Computer Assisted Radiology and Surgery)
Source: International Journal of Computer Assisted Radiology and Surgery - March 15, 2024 Category: Intensive Care Source Type: research

Test-time augmentation with synthetic data addresses distribution shifts in spectral imaging
ConclusionGiven its potential wide-ranging relevance to diverse pathologies, our approach may serve as a pivotal tool to enhance neural network generalization within the realm of spectral imaging. (Source: International Journal of Computer Assisted Radiology and Surgery)
Source: International Journal of Computer Assisted Radiology and Surgery - March 14, 2024 Category: Intensive Care Source Type: research

Cross-sectional angle prediction of lipid-rich and calcified tissue on computed tomography angiography images
ConclusionThe two methods proposed in this paper contribute to finer cross-sectional predictions of lipid-rich and calcified plaques compared to studies focusing only on longitudinal prediction. The angular prediction performance of the proposed methods outperforms the convincing conventional method for lipid-rich plaque and is comparable for calcified plaque. (Source: International Journal of Computer Assisted Radiology and Surgery)
Source: International Journal of Computer Assisted Radiology and Surgery - March 13, 2024 Category: Intensive Care Source Type: research

Regulation of AI algorithms for clinical decision support: a personal opinion
(Source: International Journal of Computer Assisted Radiology and Surgery)
Source: International Journal of Computer Assisted Radiology and Surgery - March 13, 2024 Category: Intensive Care Source Type: research

Automatic hip osteoarthritis grading with uncertainty estimation from computed tomography using digitally-reconstructed radiographs
ConclusionsIn this study, an automatic approach for grading hip OA severity from CT images was developed. The models have shown comparable performance with high ONCA, which facilitates automated grading in large-scale CT databases and indicates the potential for further disease progression analysis. Classification accuracy was correlated with the model uncertainty, which would allow for the prediction of classification errors. The code will be made publicly available athttps://github.com/NAIST-ICB/HipOA-Grading. (Source: International Journal of Computer Assisted Radiology and Surgery)
Source: International Journal of Computer Assisted Radiology and Surgery - March 12, 2024 Category: Intensive Care Source Type: research

Surgical-DINO: adapter learning of foundation models for depth estimation in endoscopic surgery
ConclusionSurgical-DINO shed some light on the successful adaptation of the foundation models into the surgical domain for depth estimation. There is clear evidence in the results that zero-shot prediction on pre-trained weights in computer vision datasets or naive fine-tuning is not sufficient to use the foundation model in the surgical domain directly. (Source: International Journal of Computer Assisted Radiology and Surgery)
Source: International Journal of Computer Assisted Radiology and Surgery - March 8, 2024 Category: Intensive Care Source Type: research