International Journal of Computer Assisted Radiology and Surgery最新文献

{"title":"Unwritten rules of healthcare assistance for robotic systems: multi-method workflow analysis of postoperative wound care treatments for the development of robotic assistive systems.","authors":"Sonja Stabenow, Franziska Jurosch, Alissa Jell, Cristina Piazza, Klaus Bengler, Dirk Wilhelm","doi":"10.1007/s11548-026-03665-5","DOIUrl":"10.1007/s11548-026-03665-5","url":null,"abstract":"<p><strong>Purpose: </strong>Understanding and modeling the context of patient care interventions, such as wound dressing changes, is essential for developing intelligent robotic assistive systems. While context awareness is widely acknowledged as a requirement in robotics research, the specific information needed to create adapted behavior remains insufficiently defined for many clinical tasks. By analyzing the wound dressing procedure, which we take as a model for care provision on a surgical ward, we aim to identify and model the requirements for such systems in terms of time and used materials, as well as other contextual factors that influence the execution of the task.</p><p><strong>Methods: </strong>We collected observational and quantitative data during postoperative wound dressing procedures to capture contextual triggers and task dependencies. Material-use tracking and communication annotations provided the data foundation. Using FRAM, we developed a sociotechnical process model that represents the workflow and its requirements. Expert interviews further contextualize the boundaries for robotic assistance.</p><p><strong>Results: </strong>Human assistance is supportive in the background, and its positive impact is beyond a mere requested helper, taking for instance the mental load of preparation. Good assistance is proactive, consultative, and adapts to the care provider. For robotic systems to become embedded in the environment, they need procedural and patient-adapted behavior to interact during wound dressing.</p><p><strong>Conclusions: </strong>Intelligent robotic systems capable of supporting healthcare professionals during patient interventions have to adapt to the rules of hospital environments. Workflow models, as presented in our work, bring human expertise to robotic autonomy.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1899-1910"},"PeriodicalIF":2.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13694337/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147977138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tumour-focused 3D-2D registration in liver laparoscopy.","authors":"Mohammad Zohaib, Erol Ozgur, Mohammad Alkhatib, Emmanuel Buc, Bertrand Le Roy, Youcef Mezouar, Adrien Bartoli","doi":"10.1007/s11548-026-03671-7","DOIUrl":"10.1007/s11548-026-03671-7","url":null,"abstract":"<p><strong>Purpose: </strong>Augmented reality (AR) is a promising tumour-resection guidance tool in liver laparoscopy. Existing methods register a preoperative liver 3D model (reconstructed from CT or MRI) to 2D laparoscopic images. They use surface landmarks to solve for registration and then transfer and overlay the internal tumour. This has two main weaknesses, hindering clinical adoption. First, computing the registration is highly challenging due to (i) multimodal and multidimensional correspondences (colourless 3D model to 2D RGB images), (ii) restricted field of view (proximity of laparoscope to parenchyma), and (iii) liver deformations (breathing and instrument interactions). Second, extrapolating from the surface to deeper tumours increases uncertainty and may result in inaccurate AR.</p><p><strong>Methods: </strong>We propose tumour-focused 3D-2D registration (Tf32), a method that registers the tumour, not the parenchyma. Tf32 exploits laparoscopic ultrasonography (LUS), which provides intraoperative cross-sections of the tumour and can be reliably localised with respect to the laparoscope. Tf32 matches the LUS tumour profile to a set of tumour profiles simulated preoperatively from the preoperative tumour 3D model. This initial registration is then refined using iterative closest point (ICP). A multiple-hypothesis implementation handles ambiguities and local minima arising from the finite profile sample set.</p><p><strong>Results: </strong>Tf32 achieved 91.07% success rate in meeting the <math><mrow><mn>10</mn> <mspace></mspace> <mtext>mm</mtext></mrow> </math> oncological margin on semi-synthetic data and established a new state-of-the-art on a public benchmark dataset.</p><p><strong>Conclusion: </strong>Compared with competitors, Tf32 does not require manual initialisation nor the full liver 3D model and is notably more accurate, making it well-adapted to clinical use.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"2035-2045"},"PeriodicalIF":2.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147788652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visual question answering-based image-finding generation for pulmonary nodules on chest CT from structured annotations.","authors":"Maiko Nagao, Kaito Urata, Atsushi Teramoto, Kazuyoshi Imaizumi, Masashi Kondo, Hiroshi Fujita","doi":"10.1007/s11548-026-03608-0","DOIUrl":"10.1007/s11548-026-03608-0","url":null,"abstract":"<p><strong>Purpose: </strong>Interpretation of imaging findings based on morphological characteristics is important for diagnosing pulmonary nodules on chest computed tomography (CT) images. In this study, we constructed a visual question answering (VQA) dataset from structured data in an open dataset and investigated an image-finding generation method for chest CT images, with the aim of enabling interactive diagnostic support that presents findings based on questions that reflect physicians' interests rather than fixed descriptions.</p><p><strong>Methods: </strong>In this study, chest CT images included in the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) datasets were used. Regions of interest surrounding the pulmonary nodules were extracted from these images, and image findings and questions were defined based on morphological characteristics recorded in the database. A dataset comprising pairs of cropped images, corresponding questions, and image findings was constructed, and the VQA model was fine-tuned on it. Language evaluation metrics such as BLEU were used to evaluate the generated image findings.</p><p><strong>Results: </strong>The VQA dataset constructed using the proposed method contained image findings with natural expressions as radiological descriptions. In addition, the generated image findings showed a high CIDEr score of 3.896, and a high agreement with the reference findings was obtained through evaluation based on morphological characteristics.</p><p><strong>Conclusion: </strong>We constructed a VQA dataset for chest CT images using structured information on the morphological characteristics from the LIDC-IDRI dataset. Methods for generating image findings in response to these questions have also been investigated. Based on the generated results and evaluation metric scores, the proposed method was effective as an interactive diagnostic support system that can present image findings according to physicians' interests.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1981-1987"},"PeriodicalIF":2.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147534343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time target tracking using image registration and motion prediction.","authors":"Victoria Buchanan, Elodie Lugez","doi":"10.1007/s11548-026-03634-y","DOIUrl":"10.1007/s11548-026-03634-y","url":null,"abstract":"<p><strong>Purpose: </strong>Real-time tracking of anatomical targets is critical in numerous clinical interventions. In magnetic resonance-guided therapies, targets are typically tracked only in 2D or pseudo-3D, and system latencies from image acquisition and processing introduce delays. This study investigates methods that leverage 2D magnetic resonance images to estimate the real-time displacement of a target from its reference position.</p><p><strong>Methods: </strong>Target displacements from their reference position on a pre-treatment image were measured using image registration. These measurements served as inputs to machine learning models that capture patient-specific motion patterns and predict subsequent displacements. Specifically, the models were trained incrementally as measurements became available, adapting to patient-specific characteristics. LightGBM, linear regression, long short-term memory (LSTM) and support vector regression (SVR) models were evaluated for their ability to perform online updates from sparse measurements and for their accuracy in forecasting upcoming target displacements. Hyperparameters were optimized using five subject datasets representative of those acquired during clinical procedures. The methods were assessed on forty-nine independent datasets.</p><p><strong>Results: </strong>Target tracking achieved a baseline registration error of 1.65 mm. Introducing system latencies ranging from 200 to 600 ms resulted in additional latency-induced errors of 0.81 to 1.98 mm. Linear regression and SVR models provided the greatest accuracy gains, reducing the latency-induced errors by 35 to 57%. In contrast, the LightGBM and LSTM models exhibited comparatively lower predictive performance, likely due to limited training data and the increased model complexity. Linear regression was the most computationally efficient, requiring only 15 ms for combined online retraining and inference in both datasets.</p><p><strong>Conclusions: </strong>Combining image registration with lightweight, patient-specific predictive modeling to mitigate system latencies can enable accurate real-time target tracking. Among the evaluated methods, linear regression and SVR achieved the most favorable balance between prediction accuracy and computational efficiency. These findings support the feasibility of robust real-time tracking without reliance on prior assumptions about target shape or motion.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"2011-2019"},"PeriodicalIF":2.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147678282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Noise levels in tele-operated robotic-assisted surgery-a phase-based analysis.","authors":"Jonas Fuchtmann, Alissa Jell, Maximilian Berlet, Helmut Friess, Dirk Wilhelm, Luca Wegener","doi":"10.1007/s11548-026-03623-1","DOIUrl":"10.1007/s11548-026-03623-1","url":null,"abstract":"<p><strong>Purpose: </strong>Robotic-assisted surgery (RAS) is becoming increasingly popular with one of its praised benefits being improved ergonomics. While other types of interventions like open or laparoscopic surgery have been assessed regarding the noise exposure and evaluations regarding the ergonomics are based on these findings, there are no studies analyzing the changed environment of tele-operated RAS.</p><p><strong>Methods: </strong>Twelve robotic interventions with an average length of 3.5 h, including (hemi-)colectomies, anterior rectum resections and sigmoidectomies were assessed using a sound level meter. Recordings were monitored by a medical expert, manually annotating individual phases of the intervention from the patient's entry till the exit of the operating room. Measured sound data were afterward mapped to one out of six corresponding surgical phases.</p><p><strong>Results: </strong>The data showed an average noise level of 60.5(±3.35) dB(A) and a L(A)eq of 63.49 dB(A) with the robotic phase being significantly, but only 0.15 dB(A) louder than the non-robotic surgical part (60.4(±3.14) dB(A)). Overall the peri-surgical phase was louder than the surgery itself with the post-surgical phase being the loudest of all phases with a level of 61.2 (±4.82) dB(A).</p><p><strong>Conclusions: </strong>Regarding sound levels tele-operated RAS seems comparable with open and laparoscopic surgery, despite the robotic phase being to a small extent louder within this evaluation. Nevertheless sound levels appear high and further reduction should be considered in order to improve ergonomics and patient safety.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1891-1898"},"PeriodicalIF":2.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13694345/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147718961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Augmented reality for intraoperative verification of surgical template placement.","authors":"Gabriele Vanni, Francesco Simi, Marina Carbone, Paolo Domenico Parchi, Vincenzo Ferrari","doi":"10.1007/s11548-026-03673-5","DOIUrl":"10.1007/s11548-026-03673-5","url":null,"abstract":"<p><strong>Purpose: </strong>Patient-specific templates (STs) are widely adopted to guide drilling and cutting in orthopaedic procedures. Their intraoperative placement typically relies on visual and tactile assessment, without feedback on positioning accuracy. This work proposes a minimal, low-cost augmented reality (AR) method for intraoperative verification of template placement, designed for the brief and constrained nature of this task.</p><p><strong>Methods: </strong>A miniature camera is rigidly mounted on the surgical template and preoperatively calibrated. During placement, live images are augmented with virtual renderings of the patient's bone model, generated from preoperative CT/MR data and displayed from the same fixed viewpoint. Correct alignment occurs only when the template reaches its planned pose, without requiring tracking systems, registration procedures, or head-mounted displays. Template placement was evaluated on a patient-specific phantom by thirteen participants, with and without AR guidance. Feasibility was further assessed through ex vivo tests on animal tissue involving seven users.</p><p><strong>Results: </strong>In phantom experiments, AR guidance was associated with improved placement accuracy (mean error 2.23 mm vs. 3.62 mm without AR, p = 0.0093). In ex vivo trials, all AR-guided osteotomy lines remained within ±2 mm of the planned trajectory suggesting experimental feasibility. However, the difference between conditions did not reach statistical significance.</p><p><strong>Conclusion: </strong>Current literature lacks simple, inexpensive, and workflow-compatible methods for verifying the placement of surgical templates. The proposed approach may provide a simple method for intraoperative visual feedback without tracking hardware or complex AR systems, potentially supporting more consistent template positioning. However, its effectiveness may depend on surgical exposure and anatomical context.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1919-1928"},"PeriodicalIF":2.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147788593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based renal artery segmentation and angle estimation for registration of endoscopic images and 3D models in robot-assisted partial nephrectomy.","authors":"Keiji Tsukino, Satoshi Kobayashi, Shunsuke Takashima, Shoko Miyauchi, Jun Mutaguchi, Shigehiro Tsukahara, Tokiyoshi Tanegashima, Shunsuke Goto, Takashi Matsumoto, Masaki Shiota, Ryo Kurazume, Masatoshi Eto","doi":"10.1007/s11548-026-03636-w","DOIUrl":"10.1007/s11548-026-03636-w","url":null,"abstract":"<p><strong>Purpose: </strong>Image-guided robot-assisted partial nephrectomy (RAPN) that incorporates three-dimensional (3D) models has improved both oncological and functional outcomes. However, registration between the physical endoscopic image and the 3D virtual model is often performed manually at the kidney level, limiting accuracy and usability. This study aimed to develop a framework built on deep learning to perform renal artery segmentation and angle estimation, enabling autonomous registration in image-guided RAPN.</p><p><strong>Methods: </strong>A total of 75 images were extracted from 50 RAPN videos. The renal arteries in these images were manually annotated and used to evaluate a U-Net segmentation model. Segmented masks by the autonomous segmentation model were approximated as ellipsoid to calculate renal artery angles. The model was evaluated using sevenfold cross-validation and further tested on endoscopic images from four unseen clinical cases, verifying its adaptability. The predicted angles were then applied to rotate the corresponding 3D kidney models, demonstrating the feasibility of autonomous registration.</p><p><strong>Results: </strong>The segmentation model achieved an average Dice similarity coefficient of 0.817 in sevenfold cross-validation. The prediction errors for the renal artery angles of four RAPN cases were RAPN1 = 0.57°, RAPN2 = 3.4°, RAPN3 = 4.9°, and RAPN4 = 4.1°. The required 3D model rotations were computed from the predicted angles: RAPN1 =  + 19.7°, RAPN2 = -19.2°, RAPN3 = -28.4°, and RAPN4 = -13.3°.</p><p><strong>Conclusions: </strong>These findings demonstrate that deep learning-based segmentation and angle estimation of the renal artery can be performed accurately, providing a foundation for autonomous registration in image-guided RAPN.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1989-1998"},"PeriodicalIF":2.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147788567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A fully automated CT-based pelvimetry pipeline for quantifying mid-pelvic surgical workspace in rectal cancer.","authors":"Shih-Feng Huang, Hsin-Ping Tseng, Chao-Wen Hsu","doi":"10.1007/s11548-026-03606-2","DOIUrl":"10.1007/s11548-026-03606-2","url":null,"abstract":"<p><strong>Purpose: </strong>Pelvimetry may aid preoperative planning in rectal cancer surgery, yet manual measurements are time-consuming and MRI-based methods require dedicated protocols. We developed a fully automated CT-based pipeline for quantifying mid-pelvic geometry and soft tissue occupancy at the ischial spine level, the narrowest corridor encountered during total mesorectal excision.</p><p><strong>Methods: </strong>This retrospective feasibility study included 73 patients with mid-to-low rectal cancer who underwent contrast-enhanced staging CT. Automated segmentation was performed using TotalSegmentator. The pipeline extracted interspinous distance (ISD) via an anatomically anchored search strategy with valley detection, constructed a posterior pelvic triangle bounded by the bilateral ischial spines and anterior sacrum, and quantified bowel and fat occupancy within this region. Automated measurements were validated against blinded manual annotations by two independent raters. Pipeline success rates, failure taxonomy, and agreement between contrast-enhanced and non-contrast acquisitions (n = 69 paired cases) were evaluated using intraclass correlation coefficients (ICC) and Bland-Altman analysis.</p><p><strong>Results: </strong>The pipeline achieved complete ISD extraction in 100% of contrast-enhanced and 95.8% of non-contrast cases. In blinded validation (n = 70), automated ISD agreement against manual reference (ICC = 0.977; bias = 0.45 mm) exceeded inter-rater reliability (ICC = 0.962), and triangle-derived metrics showed a good-to-excellent agreement (ICC = 0.86-0.89, auto vs. manual). ISD measurements showed excellent agreement across contrast conditions (ICC = 0.99; mean bias = 0.09 mm; 95% limits of agreement: - 2.51 to 2.69 mm). Bone-derived triangle metrics demonstrated strong concordance (ICC = 0.90-0.93). Fat-related metrics showed systematic differences between contrast conditions but maintained good agreement (ICC = 0.87-0.91). Sex-based differences were consistent with known pelvic dimorphism.</p><p><strong>Conclusion: </strong>This fully automated pipeline reliably extracts mid-pelvic geometry and soft tissue metrics from routine staging CT, with measurement agreement matching or exceeding manual inter-rater variability, offering a standardized, practical, and reproducible approach for characterizing the surgical workspace in rectal cancer patients.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1999-2010"},"PeriodicalIF":2.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147624684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Couinaud segment-aware deep learning on point clouds for major liver resection planning.","authors":"Joy Rakshit, Janine Rothert, Georg Hille, Tobias Huber, Hauke Lang, Rabea Margies, Florentine Huettl, Sylvia Saalfeld","doi":"10.1007/s11548-026-03663-7","DOIUrl":"10.1007/s11548-026-03663-7","url":null,"abstract":"<p><strong>Purpose: </strong>In this study, we address the problem of automatic liver resection planning for major surgical procedures, including hemi-hepatectomy and extended hemi-hepatectomy, using deep learning. Motivated by clinical practice, where Couinaud liver segments are routinely used to describe tumor location and guide surgical decision-making, we investigate whether incorporating this anatomical information can improve model performance and clinical relevance.</p><p><strong>Methods: </strong>We propose a point cloud-based geometric deep learning approach based on a modified RandLA-Net architecture to predict liver resection zones. The model was trained and evaluated on 70 hemi-hepatectomy cases from Johannes Gutenberg University, Mainz, Germany (internal dataset). Two composite loss functions were evaluated: cross-entropy (CE) combined with intersection over union (IoU) and CE combined with Dice loss. For each loss function, models were trained with and without Couinaud segment information. Generalizability was assessed on an external dataset of 30 hemi-hepatectomy cases from the colorectal liver metastases (CRLM) cohort.</p><p><strong>Results: </strong>Both loss functions achieved comparable performance across the evaluated datasets, with CE <math><mo>+</mo></math> IoU consistently outperforming CE <math><mo>+</mo></math> Dice. On the internal test set, incorporating Couinaud segment information increased the IoU_mean from 0.787 to 0.804 and the F1-score from 0.864 to 0.870. A Wilcoxon signed-rank test on 15 paired cases confirmed a statistically significant improvement in IoU_mean (p = 0.030), with 80% of cases showing improvement. On the external dataset, IoU_mean improved from 0.666 to 0.702 and F1-score from 0.786 to 0.799 when Couinaud information was included. Excluding five anatomically complex cases, a Wilcoxon signed-rank test on the remaining 25 paired cases showed a significant improvement in IoU_mean (p = 0.019), with 68% of cases demonstrating improved performance.</p><p><strong>Conclusion: </strong>Explicit integration of Couinaud segment information improves both quantitative performance and clinical relevance in automatic major liver resection planning, particularly by better preserving critical vascular structures.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1939-1948"},"PeriodicalIF":2.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13694551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147857683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A usability-driven workflow for virtual fracture reduction using an open platform with ICP.","authors":"Costanza Manfredi, Gabriele Vanni, Marina Carbone, Eleonora Tiribilli, Elena Denisova, Vincenzo Ferrari","doi":"10.1007/s11548-026-03621-3","DOIUrl":"10.1007/s11548-026-03621-3","url":null,"abstract":"<p><strong>Purpose: </strong>Virtual surgical planning can support fracture reduction, but its routine use remains limited by the time and interaction burden associated with manually manipulating fragments in six degrees of freedom. This work presents a CBCT-based workflow designed to minimise user interaction by replacing continuous manual manipulation with a small number of point-based initialisation steps.</p><p><strong>Methods: </strong>Fragment alignment through a sequential rigid registration strategy based on the iterative closest point (ICP) algorithm. The largest fragment is initialised manually. Subsequent fragments inherit the transformation estimated in the previous step and are refined automatically through ICP, although additional initialisation may be required in unfavourable configurations. Reduction accuracy was assessed using surface-to-surface distance analysis. The workflow is implemented using fully open-source tools ensuring reproducibility and accessibility without proprietary dependencies.</p><p><strong>Results: </strong>The workflow was evaluated on experimentally induced bovine femur fractures comprising two to four fragments by users with heterogeneous backgrounds. Compared to manual alignment, the proposed approach significantly reduced reduction time (Wilcoxon signed-rank paired test, p = 0.022), corresponding to an average reduction of approximately 63%. Surface-to-surface analysis showed mean alignment errors of approximately 1.5-1.8 mm, with consistent results across users.</p><p><strong>Conclusion: </strong>A usability-driven workflow that constrains interaction to a limited number of discrete steps can substantially improve the efficiency of virtual fracture reduction while maintaining user control. The open-source pipeline provides an accessible solution for preoperative fracture reduction planning and should be regarded as a foundation for further developments in usability-driven virtual fracture reduction.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1963-1972"},"PeriodicalIF":2.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147724480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}