Nested ResNet: A Vision-Based Method for Detecting the Sensing Area of a Drop-in Gamma Probe

• URL: http://arxiv.org/abs/2410.23154v1
• Date: Wed, 30 Oct 2024 16:08:43 GMT
• Title: Nested ResNet: A Vision-Based Method for Detecting the Sensing Area of a Drop-in Gamma Probe
• Authors: Songyu Xu, Yicheng Hu, Jionglong Su, Daniel Elson, Baoru Huang,
• Abstract summary: Drop-in gamma probes are widely used in robotic-assisted minimally invasive surgery (RAMIS) for lymph node detection. Previous work attempted to predict the sensing area location using laparoscopic images, but the prediction accuracy was unsatisfactory. We introduce a three-branch deep learning framework to predict the sensing area of the probe.
• Score: 2.835688998859888
• License:
• Abstract: Purpose: Drop-in gamma probes are widely used in robotic-assisted minimally invasive surgery (RAMIS) for lymph node detection. However, these devices only provide audio feedback on signal intensity, lacking the visual feedback necessary for precise localisation. Previous work attempted to predict the sensing area location using laparoscopic images, but the prediction accuracy was unsatisfactory. Improvements are needed in the deep learning-based regression approach. Methods: We introduce a three-branch deep learning framework to predict the sensing area of the probe. Specifically, we utilise the stereo laparoscopic images as input for the main branch and develop a Nested ResNet architecture. The framework also incorporates depth estimation via transfer learning and orientation guidance through probe axis sampling. The combined features from each branch enhanced the accuracy of the prediction. Results: Our approach has been evaluated on a publicly available dataset, demonstrating superior performance over previous methods. In particular, our method resulted in a 22.10\% decrease in 2D mean error and a 41.67\% reduction in 3D mean error. Additionally, qualitative comparisons further demonstrated the improved precision of our approach. Conclusion: With extensive evaluation, our solution significantly enhances the accuracy and reliability of sensing area predictions. This advancement enables visual feedback during the use of the drop-in gamma probe in surgery, providing surgeons with more accurate and reliable localisation.}

Related papers

• NeRF-Det++: Incorporating Semantic Cues and Perspective-aware Depth Supervision for Indoor Multi-View 3D Detection [72.0098999512727]
  NeRF-Det has achieved impressive performance in indoor multi-view 3D detection by utilizing NeRF to enhance representation learning. We present three corresponding solutions, including semantic enhancement, perspective-aware sampling, and ordinal depth supervision. The resulting algorithm, NeRF-Det++, has exhibited appealing performance in the ScanNetV2 and AR KITScenes datasets.
  arXiv  Detail & Related papers  (2024-02-22T11:48:06Z)
• Multi-task learning with cross-task consistency for improved depth estimation in colonoscopy [0.2995885872626565]
  We develop a novel multi-task learning (MTL) approach with a shared encoder and two decoders, namely a surface normal decoder and a depth estimator. We demonstrate an improvement of 14.17% on relative error and 10.4% on $delta_1$ accuracy over the most accurate baseline state-of-the-art BTS approach.
  arXiv  Detail & Related papers  (2023-11-30T16:13:17Z)
• The Impact of Loss Functions and Scene Representations for 3D/2D Registration on Single-view Fluoroscopic X-ray Pose Estimation [1.758213853394712]
  We first develop a differentiable projection rendering framework for the efficient computation of Digitally Reconstructed Radiographs (DRRs) We then perform pose estimation by iterative descent using various candidate loss functions, that quantify the image discrepancy of the synthesized DRR with respect to the ground-truth fluoroscopic X-ray image. Using the Mutual Information loss, a comprehensive evaluation of pose estimation performed on a tomographic X-ray dataset of 50 patients$'$ skulls shows that utilizing either discretized (CBCT) or neural (NeTT/mNeRF) scene representations in DiffProj leads to
  arXiv  Detail & Related papers  (2023-08-01T01:12:29Z)
• UncLe-SLAM: Uncertainty Learning for Dense Neural SLAM [60.575435353047304]
  We present an uncertainty learning framework for dense neural simultaneous localization and mapping (SLAM) We propose an online framework for sensor uncertainty estimation that can be trained in a self-supervised manner from only 2D input data.
  arXiv  Detail & Related papers  (2023-06-19T16:26:25Z)
• Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization [81.29406957201458]
  Existing detection methods commonly use a parameterized bounding box (BBox) to model and detect (horizontal) objects. We argue that such a mechanism has fundamental limitations in building an effective regression loss for rotation detection. We propose to model the rotated objects as Gaussian distributions. We extend our approach from 2-D to 3-D with a tailored algorithm design to handle the heading estimation.
  arXiv  Detail & Related papers  (2022-09-22T07:50:48Z)
• The KFIoU Loss for Rotated Object Detection [115.334070064346]
  In this paper, we argue that one effective alternative is to devise an approximate loss who can achieve trend-level alignment with SkewIoU loss. Specifically, we model the objects as Gaussian distribution and adopt Kalman filter to inherently mimic the mechanism of SkewIoU. The resulting new loss called KFIoU is easier to implement and works better compared with exact SkewIoU.
  arXiv  Detail & Related papers  (2022-01-29T10:54:57Z)
• RVMDE: Radar Validated Monocular Depth Estimation for Robotics [5.360594929347198]
  An innate rigid calibration of binocular vision sensors is crucial for accurate depth estimation. Alternatively, a monocular camera alleviates the limitation at the expense of accuracy in estimating depth, and the challenge exacerbates in harsh environmental conditions. This work explores the utility of coarse signals from radar when fused with fine-grained data from a monocular camera for depth estimation in harsh environmental conditions.
  arXiv  Detail & Related papers  (2021-09-11T12:02:29Z)
• Probabilistic and Geometric Depth: Detecting Objects in Perspective [78.00922683083776]
  3D object detection is an important capability needed in various practical applications such as driver assistance systems. Monocular 3D detection, as an economical solution compared to conventional settings relying on binocular vision or LiDAR, has drawn increasing attention recently but still yields unsatisfactory results. This paper first presents a systematic study on this problem and observes that the current monocular 3D detection problem can be simplified as an instance depth estimation problem.
  arXiv  Detail & Related papers  (2021-07-29T16:30:33Z)
• Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images [15.7026400415269]
  We propose a novel learning-to-learn framework for landmark detection. Our proposed framework is general and shows the potential to improve the efficiency of anatomical landmark detection.
  arXiv  Detail & Related papers  (2021-05-19T13:39:18Z)
• Dense Label Encoding for Boundary Discontinuity Free Rotation Detection [69.75559390700887]
  This paper explores a relatively less-studied methodology based on classification. We propose new techniques to push its frontier in two aspects. Experiments and visual analysis on large-scale public datasets for aerial images show the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-11-19T05:42:02Z)
• Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving [12.310862288230075]
  This work presents a probabilistic deep neural network that combines LiDAR point clouds and RGB camera images for robust, accurate 3D object detection. We explicitly model uncertainties in the classification and regression tasks, and leverage uncertainties to train the fusion network via a sampling mechanism.
  arXiv  Detail & Related papers  (2020-02-01T14:24:51Z)

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.