ViTALS: Vision Transformer for Action Localization in Surgical Nephrectomy

• URL: http://arxiv.org/abs/2405.02571v1
• Date: Sat, 4 May 2024 05:07:39 GMT
• Title: ViTALS: Vision Transformer for Action Localization in Surgical Nephrectomy
• Authors: Soumyadeep Chandra, Sayeed Shafayet Chowdhury, Courtney Yong, Chandru P. Sundaram, Kaushik Roy,
• Abstract summary: We introduce a new dataset of nephrectomy surgeries called UroSlice. To perform the action localization from these videos, we propose a novel model termed as ViTALS' Our model incorporates hierarchical dilated temporal convolution layers and inter-layer residual connections to capture the temporal correlations at finer as well as coarser granularities.
• Score: 7.145773305697571
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Surgical action localization is a challenging computer vision problem. While it has promising applications including automated training of surgery procedures, surgical workflow optimization, etc., appropriate model design is pivotal to accomplishing this task. Moreover, the lack of suitable medical datasets adds an additional layer of complexity. To that effect, we introduce a new complex dataset of nephrectomy surgeries called UroSlice. To perform the action localization from these videos, we propose a novel model termed as `ViTALS' (Vision Transformer for Action Localization in Surgical Nephrectomy). Our model incorporates hierarchical dilated temporal convolution layers and inter-layer residual connections to capture the temporal correlations at finer as well as coarser granularities. The proposed approach achieves state-of-the-art performance on Cholec80 and UroSlice datasets (89.8% and 66.1% accuracy, respectively), validating its effectiveness.

Related papers

• Efficient Deformable Tissue Reconstruction via Orthogonal Neural Plane [58.871015937204255]
  We introduce Fast Orthogonal Plane (plane) for the reconstruction of deformable tissues. We conceptualize surgical procedures as 4D volumes, and break them down into static and dynamic fields comprised of neural planes. This factorization iscretizes four-dimensional space, leading to a decreased memory usage and faster optimization.
  arXiv  Detail & Related papers  (2023-12-23T13:27:50Z)
• Phase-Specific Augmented Reality Guidance for Microscopic Cataract Surgery Using Long-Short Spatiotemporal Aggregation Transformer [14.568834378003707]
  Phaemulsification cataract surgery (PCS) is a routine procedure using a surgical microscope. PCS guidance systems extract valuable information from surgical microscopic videos to enhance proficiency. Existing PCS guidance systems suffer from non-phasespecific guidance, leading to redundant visual information. We propose a novel phase-specific augmented reality (AR) guidance system, which offers tailored AR information corresponding to the recognized surgical phase.
  arXiv  Detail & Related papers  (2023-09-11T02:56:56Z)
• Visual-Kinematics Graph Learning for Procedure-agnostic Instrument Tip Segmentation in Robotic Surgeries [29.201385352740555]
  We propose a novel visual-kinematics graph learning framework to accurately segment the instrument tip given various surgical procedures. Specifically, a graph learning framework is proposed to encode relational features of instrument parts from both image and kinematics. A cross-modal contrastive loss is designed to incorporate robust geometric prior from kinematics to image for tip segmentation.
  arXiv  Detail & Related papers  (2023-09-02T14:52:58Z)
• Cross-Dataset Adaptation for Instrument Classification in Cataract Surgery Videos [54.1843419649895]
  State-of-the-art models, which perform this task well on a particular dataset, perform poorly when tested on another dataset. We propose a novel end-to-end Unsupervised Domain Adaptation (UDA) method called the Barlow Adaptor. In addition, we introduce a novel loss called the Barlow Feature Alignment Loss (BFAL) which aligns features across different domains.
  arXiv  Detail & Related papers  (2023-07-31T18:14:18Z)
• GLSFormer : Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos [57.93194315839009]
  We propose a vision transformer-based approach to learn temporal features directly from sequence-level patches. We extensively evaluate our approach on two cataract surgery video datasets, Cataract-101 and D99, and demonstrate superior performance compared to various state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-07-20T17:57:04Z)
• Neural LerPlane Representations for Fast 4D Reconstruction of Deformable Tissues [52.886545681833596]
  LerPlane is a novel method for fast and accurate reconstruction of surgical scenes under a single-viewpoint setting. LerPlane treats surgical procedures as 4D volumes and factorizes them into explicit 2D planes of static and dynamic fields. LerPlane shares static fields, significantly reducing the workload of dynamic tissue modeling.
  arXiv  Detail & Related papers  (2023-05-31T14:38:35Z)
• Surgical-VQLA: Transformer with Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery [18.248882845789353]
  We develop a surgical question-answering system to facilitate robot-assisted surgical scene and activity understanding from recorded videos. Most of the existing VQA methods require an object detector and regions based feature extractor to extract visual features and fuse them with the embedded text of the question for answer generation. We propose Visual Question Localized-Answering in Robotic Surgery (Surgical-VQLA) to localize the specific surgical area during the answer prediction.
  arXiv  Detail & Related papers  (2023-05-19T14:13:47Z)
• E-DSSR: Efficient Dynamic Surgical Scene Reconstruction with Transformer-based Stereoscopic Depth Perception [15.927060244702686]
  We present an efficient reconstruction pipeline for highly dynamic surgical scenes that runs at 28 fps. Specifically, we design a transformer-based stereoscopic depth perception for efficient depth estimation. We evaluate the proposed pipeline on two datasets, the public Hamlyn Centre Endoscopic Video dataset and our in-house DaVinci robotic surgery dataset.
  arXiv  Detail & Related papers  (2021-07-01T05:57:41Z)
• Multimodal Semantic Scene Graphs for Holistic Modeling of Surgical Procedures [70.69948035469467]
  We take advantage of the latest computer vision methodologies for generating 3D graphs from camera views. We then introduce the Multimodal Semantic Graph Scene (MSSG) which aims at providing unified symbolic and semantic representation of surgical procedures.
  arXiv  Detail & Related papers  (2021-06-09T14:35:44Z)
• Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer [57.18185972461453]
  We introduce for the first time in surgical workflow analysis Transformer to reconsider the ignored complementary effects of spatial and temporal features for accurate phase recognition. Our framework is lightweight and processes the hybrid embeddings in parallel to achieve a high inference speed.
  arXiv  Detail & Related papers  (2021-03-17T15:12:55Z)

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.