Self-supervised Mamba-based Mastoidectomy Shape Prediction for Cochlear Implant Surgery

• URL: http://arxiv.org/abs/2407.15787v4
• Date: Fri, 28 Feb 2025 19:08:49 GMT
• Title: Self-supervised Mamba-based Mastoidectomy Shape Prediction for Cochlear Implant Surgery
• Authors: Yike Zhang, Eduardo Davalos, Dingjie Su, Ange Lou, Jack H. Noble,
• Abstract summary: We propose a novel Mamba-based method to synthesize the mastoidectomy volume using only preoperative Computed Tomography (CT) scans.<n>Our approach introduces a self-supervised learning framework designed to predict the mastoidectomy shape and reconstruct a 3D post-mastoidectomy surface.<n>Our method achieves a mean Dice score of 0.70 in estimating mastoidectomy regions, demonstrating its effectiveness for accurate and efficient surgical preoperative planning.
• Score: 3.626734411913593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cochlear Implant (CI) procedures require the insertion of an electrode array into the cochlea within the inner ear. To achieve this, mastoidectomy, a surgical procedure involving the removal of part of the mastoid region of the temporal bone using a high-speed drill provides safe access to the cochlea through the middle and inner ear. In this paper, we propose a novel Mamba-based method to synthesize the mastoidectomy volume using only preoperative Computed Tomography (CT) scans, where the mastoid remains intact. Our approach introduces a self-supervised learning framework designed to predict the mastoidectomy shape and reconstruct a 3D post-mastoidectomy surface directly from preoperative CT scans. This reconstruction aligns with intraoperative microscope views, enabling various downstream surgical applications. For training, we leverage postoperative CT scans to bypass manual data cleaning and labeling, even when the region removed during mastoidectomy is affected by challenges such as metal artifacts, low signal-to-noise ratio, or electrode wiring. Our method achieves a mean Dice score of 0.70 in estimating mastoidectomy regions, demonstrating its effectiveness for accurate and efficient surgical preoperative planning.

Related papers

• Semantic Segmentation for Preoperative Planning in Transcatheter Aortic Valve Replacement [61.573750959726475]
  We consider medical guidelines for preoperative planning of the transcatheter aortic valve replacement (TAVR) and identify tasks that may be supported via semantic segmentation models.<n>We first derive fine-grained TAVR-relevant pseudo-labels from coarse-grained anatomical information, in order to train segmentation models and quantify how well they are able to find these structures in the scans.
  arXiv  Detail & Related papers  (2025-07-22T13:24:45Z)
• Weakly-supervised Mamba-Based Mastoidectomy Shape Prediction for Cochlear Implant Surgery Using 3D T-Distribution Loss [4.777201894011511]
  We propose a weakly-supervised Mamba-based framework to predict accurate mastoidectomy regions directly from preoperative CT scans.<n>Our approach utilizes a 3D T-Distribution loss function inspired by the Student-t distribution, which effectively handles the complex geometric variability inherent in mastoidectomy shapes.<n>The proposed method is extensively evaluated against state-of-the-art approaches, demonstrating superior performance in predicting accurate and clinically relevant mastoidectomy regions.
  arXiv  Detail & Related papers  (2025-05-23T20:53:03Z)
• SSDD-GAN: Single-Step Denoising Diffusion GAN for Cochlear Implant Surgical Scene Completion [4.250558597144547]
  We propose an efficient method to complete the surgical scene of the synthetic postmastoidectomy dataset. Our approach leverages self-supervised learning on real surgical datasets to train a Single-Step Denoising Diffusion-GAN (SSDD-GAN) The trained model is then directly applied to the synthetic postmastoidectomy dataset using a zero-shot approach.
  arXiv  Detail & Related papers  (2025-02-08T22:04:22Z)
• Topology-based deep-learning segmentation method for deep anterior lamellar keratoplasty (DALK) surgical guidance using M-mode OCT data [0.0]
  We develop a topology-based deep-learning segmentation method that integrates a topological loss function with a modified network architecture. This approach effectively reduces the effects of noise and improves segmentation speed, precision, and stability.
  arXiv  Detail & Related papers  (2025-01-07T19:57:15Z)
• Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation [47.119513326344126]
  The BraTS-MEN-RT challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of radiotherapy planning brain MRIs. Each case includes a defaced 3D post-contrast T1-weighted radiotherapy planning MRI in its native acquisition space. Target volume annotations adhere to established radiotherapy planning protocols.
  arXiv  Detail & Related papers  (2024-05-28T17:25:43Z)
• Monocular Microscope to CT Registration using Pose Estimation of the Incus for Augmented Reality Cochlear Implant Surgery [3.8909273404657556]
  We develop a method that permits direct 2D-to-3D registration of the view microscope video to the pre-operative Computed Tomography (CT) scan without the need for external tracking equipment. Our results demonstrate the accuracy with an average rotation error of less than 25 degrees and a translation error of less than 2 mm, 3 mm, and 0.55% for the x, y, and z axes, respectively.
  arXiv  Detail & Related papers  (2024-03-12T00:26:08Z)
• An Endoscopic Chisel: Intraoperative Imaging Carves 3D Anatomical Models [8.516340459721484]
  We propose a first vision-based approach to update the preoperative 3D anatomical model. Results show a decrease in error during surgical progression as opposed to increasing when no update is employed.
  arXiv  Detail & Related papers  (2024-02-19T05:06:52Z)
• AiAReSeg: Catheter Detection and Segmentation in Interventional Ultrasound using Transformers [75.20925220246689]
  endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature. This work proposes a solution using an adaptation of a state-of-the-art machine learning transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences.
  arXiv  Detail & Related papers  (2023-09-25T19:34:12Z)
• FocalErrorNet: Uncertainty-aware focal modulation network for inter-modal registration error estimation in ultrasound-guided neurosurgery [3.491999371287298]
  Intra-operative tissue deformation (called brain shift) can move the surgical target and render the pre-surgical plan invalid. We propose a novel deep learning technique based on 3D focal modulation in conjunction with uncertainty estimation to accurately assess MRI-iUS registration errors for brain tumor surgery.
  arXiv  Detail & Related papers  (2023-07-26T21:42:22Z)
• Safe Deep RL for Intraoperative Planning of Pedicle Screw Placement [61.28459114068828]
  We propose an intraoperative planning approach for robotic spine surgery that leverages real-time observation for drill path planning based on Safe Deep Reinforcement Learning (DRL) Our approach was capable of achieving 90% bone penetration with respect to the gold standard (GS) drill planning.
  arXiv  Detail & Related papers  (2023-05-09T11:42:53Z)
• Live image-based neurosurgical guidance and roadmap generation using unsupervised embedding [53.992124594124896]
  We present a method for live image-only guidance leveraging a large data set of annotated neurosurgical videos. A generated roadmap encodes the common anatomical paths taken in surgeries in the training set. We trained and evaluated the proposed method with a data set of 166 transsphenoidal adenomectomy procedures.
  arXiv  Detail & Related papers  (2023-03-31T12:52:24Z)
• Learning-Based Keypoint Registration for Fetoscopic Mosaicking [65.02392513942533]
  In Twin-to-Twin Transfusion Syndrome (TTTS), abnormal vascular anastomoses in the monochorionic placenta can produce uneven blood flow between the two fetuses. We propose a learning-based framework for in-vivo fetoscopy frame registration for field-of-view expansion.
  arXiv  Detail & Related papers  (2022-07-26T21:21:12Z)
• Surgical Phase Recognition in Laparoscopic Cholecystectomy [57.929132269036245]
  We propose a Transformer-based method that utilizes calibrated confidence scores for a 2-stage inference pipeline. Our method outperforms the baseline model on the Cholec80 dataset, and can be applied to a variety of action segmentation methods.
  arXiv  Detail & Related papers  (2022-06-14T22:55:31Z)
• Automatic Detection and Segmentation of Postoperative Cerebellar Damage Based on Normalization [1.1470070927586016]
  A reliable localization and measure of cerebellar damage is fundamental to study the relationship between the damaged cerebellar regions and postoperative neurological outcomes. Existing cerebellum normalization methods are not reliable on postoperative scans, therefore current approaches to measure surgical damage rely on manual labelling. We develop a robust algorithm to automatically detect and measure cerebellum damage due to surgery using postoperative 3D T1 magnetic resonance imaging.
  arXiv  Detail & Related papers  (2022-03-03T22:26:59Z)
• A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning [56.52933974838905]
  Current medical workflow requires manual delineation of organs-at-risk (OAR) In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation. Our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging.
  arXiv  Detail & Related papers  (2022-03-01T17:08:41Z)
• AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo [55.04215695343928]
  The aortic vessel tree is composed of the aorta and its branching arteries. We systematically review computing techniques for the automatic and semi-automatic segmentation of the aortic vessel tree.
  arXiv  Detail & Related papers  (2021-08-06T08:18:28Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.