Towards image-guided, minimally-invasive robotic surgery for partial nephrectomy
Notice bibliographique
Résumé
Introduction: Surgery remains one of the primary methods for terminating cancerous tumours. Minimally-invasive robotic surgery, in particular, provides several benefits, such as filtering of hand tremor, offering more complex and flexible manipulation capabilities that lead to increased dexterity and higher precisions, and more comfortable seating for the surgeon. All in turn lead to reduced blood loss, lower infection and complication rates, less post-operative pain, shorter hospital stays, and better overall surgical outcomes. Pre-operative 3D medical imaging modalities, mainly magnetic resonance imaging (MRI) and computed tomography (CT) are used for surgical planning, in which tumour excision margins are identified for maximal sparing of healthy tissue. However, transferring such plans from the pre-operative frame-of-reference to the dynamic intra-operative scene remains a necessary yet largely unsolved problem. We summarize our team's progress towards addressing this problem focusing on partial nephrectomy (RAPN) performed with a daVinci surgical robot. Method: We perform pre-operative 3D image segmentation of the tumour and surrounding healthy tissue using interactive random walker image segmentation, which provides an uncertainty-encoding segmentation used to construct a 3D model of the segmented patient anatomy. We reconstruct the 3D geometry of the surgical scene from the stereo endoscopic video, regularized by the patient-specific shape prior. We process the endoscopic images to detect tissue boundaries and other features. Then we align, first via rigid then via deformable registration, the pre-operative segmentation to the 3D reconstructed scene and the endoscopic image features. Finally, we present to the surgeon an augmented reality view showing an overlay of the tumour resection targets on top of the endoscopic view, in a way that depicts uncertainty in localizing the tumour boundary. Material: We collected pre-operative and intra-operative patient data in the context of RAPN including stereo endoscopic video at full HD 1080i (da Vinci S HD Surgical System), CT images (Siemens CT Sensation 16 and 64 slices), MR images (Siemens MRI Avanto 1.5T), and US images (Ultrasonix SonixTablet with a flexible laparoscopic linear probe). We also acquired CT images and stereo video from in-silico phantoms and ex-vivo lamb kidneys with artificial tumours for test and validation purposes. Results and Discussion: We successfully developed a novel proof-of-concept framework for prior and uncertainty encoded augmented reality system that fuses pre-operative patient specific information into the intra-operative surgical scene. Preliminary studies and initial surgeons' feedback on the developed augmented reality system are encouraging. Our future work will focus on investigating the use of intra-operative US data in our system to leverage all imaging modalities available during surgeries. Before a full system integration of these components, improving accuracy and speed of aforementioned algorithms, and the intuitiveness of the augmented reality visualization, remain active research projects for our team.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,009 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».