WeakSurg: Weakly supervised surgical instrument segmentation using temporal equivariance and semantic continuity

• URL: http://arxiv.org/abs/2403.09551v1
• Date: Thu, 14 Mar 2024 16:39:11 GMT
• Title: WeakSurg: Weakly supervised surgical instrument segmentation using temporal equivariance and semantic continuity
• Authors: Qiyuan Wang, Yanzhe Liu, Shang Zhao, Rong Liu, S. Kevin Zhou,
• Abstract summary: WeakSurg is first instrument-presence-only weakly supervised segmentation architecture to take temporal information into account for surgical scenarios. Our results show that WeakSurg compares favorably with state-of-the-art methods not only on semantic segmentation metrics but also on instance segmentation metrics.
• Score: 14.448593791011204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly supervised surgical instrument segmentation with only instrument presence labels has been rarely explored in surgical domain. To mitigate the highly under-constrained challenges, we extend a two-stage weakly supervised segmentation paradigm with temporal attributes from two perspectives. From a temporal equivariance perspective, we propose a prototype-based temporal equivariance regulation loss to enhance pixel-wise consistency between adjacent features. From a semantic continuity perspective, we propose a class-aware temporal semantic continuity loss to constrain the semantic consistency between a global view of target frame and local non-discriminative regions of adjacent reference frame. To the best of our knowledge, WeakSurg is the first instrument-presence-only weakly supervised segmentation architecture to take temporal information into account for surgical scenarios. Extensive experiments are validated on Cholec80, an open benchmark for phase and instrument recognition. We annotate instance-wise instrument labels with fixed time-steps which are double checked by a clinician with 3-years experience. Our results show that WeakSurg compares favorably with state-of-the-art methods not only on semantic segmentation metrics but also on instance segmentation metrics.

Related papers

• GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features [68.14842693208465]
  GeneralAD is an anomaly detection framework designed to operate in semantic, near-distribution, and industrial settings. We propose a novel self-supervised anomaly generation module that employs straightforward operations like noise addition and shuffling to patch features. We extensively evaluated our approach on ten datasets, achieving state-of-the-art results in six and on-par performance in the remaining.
  arXiv  Detail & Related papers  (2024-07-17T09:27:41Z)
• SLoRD: Structural Low-Rank Descriptors for Shape Consistency in Vertebrae Segmentation [13.225110742269543]
  We propose a contour-based network for automatic and precise segmentation of vertebrae from CT images. Specifically, a contour-based network is proposed based on Structural Low-Rank Descriptors for shape consistency, termed SLoRD. For a more precise representation of contour descriptors, we adopt the spherical coordinate system and devise the spherical centroid.
  arXiv  Detail & Related papers  (2024-07-11T14:39:54Z)
• PWISeg: Point-based Weakly-supervised Instance Segmentation for Surgical Instruments [27.89003436883652]
  We propose a weakly-supervised surgical instrument segmentation approach, named Point-based Weakly-supervised Instance (PWISeg) PWISeg adopts an FCN-based architecture with point-to-box and point-to-mask branches to model the relationships between feature points and bounding boxes. Based on this, we propose a key pixel association loss and a key pixel distribution loss, driving the point-to-mask branch to generate more accurate segmentation predictions.
  arXiv  Detail & Related papers  (2023-11-16T11:48:29Z)
• DIR-AS: Decoupling Individual Identification and Temporal Reasoning for Action Segmentation [84.78383981697377]
  Fully supervised action segmentation works on frame-wise action recognition with dense annotations and often suffers from the over-segmentation issue. We develop a novel local-global attention mechanism with temporal pyramid dilation and temporal pyramid pooling for efficient multi-scale attention. We achieve state-of-the-art accuracy, eg, 82.8% (+2.6%) on GTEA and 74.7% (+1.2%) on Breakfast, which demonstrates the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2023-04-04T20:27:18Z)
• A Survey on Label-efficient Deep Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction [115.9169213834476]
  This paper offers a comprehensive review on label-efficient segmentation methods. We first develop a taxonomy to organize these methods according to the supervision provided by different types of weak labels. Next, we summarize the existing label-efficient segmentation methods from a unified perspective.
  arXiv  Detail & Related papers  (2022-07-04T06:21:01Z)
• Temporal Transductive Inference for Few-Shot Video Object Segmentation [27.140141181513425]
  Few-shot object segmentation (FS-VOS) aims at segmenting video frames using a few labelled examples of classes not seen during initial training. Key to our approach is the use of both global and local temporal constraints. Empirically, our model outperforms state-of-the-art meta-learning approaches in terms of mean intersection over union on YouTube-VIS by 2.8%.
  arXiv  Detail & Related papers  (2022-03-27T14:08:30Z)
• TraSeTR: Track-to-Segment Transformer with Contrastive Query for Instance-level Instrument Segmentation in Robotic Surgery [60.439434751619736]
  We propose TraSeTR, a Track-to-Segment Transformer that exploits tracking cues to assist surgical instrument segmentation. TraSeTR jointly reasons about the instrument type, location, and identity with instance-level predictions. The effectiveness of our method is demonstrated with state-of-the-art instrument type segmentation results on three public datasets.
  arXiv  Detail & Related papers  (2022-02-17T05:52:18Z)
• Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy [68.86835407617778]
  Anomaly Transformer achieves state-of-the-art performance on six unsupervised time series anomaly detection benchmarks. Anomaly Transformer achieves state-of-the-art performance on six unsupervised time series anomaly detection benchmarks.
  arXiv  Detail & Related papers  (2021-10-06T10:33:55Z)
• Disentangling Human Error from the Ground Truth in Segmentation of Medical Images [12.009437407687987]
  We present a method for jointly learning, from purely noisy observations alone, the reliability of individual annotators and the true segmentation label distributions. We demonstrate the utility of the method on three public medical imaging segmentation datasets with simulated (when necessary) and real diverse annotations.
  arXiv  Detail & Related papers  (2020-07-31T11:03:12Z)
• Weakly Supervised Temporal Action Localization with Segment-Level Labels [140.68096218667162]
  Temporal action localization presents a trade-off between test performance and annotation-time cost. We introduce a new segment-level supervision setting: segments are labeled when annotators observe actions happening here. We devise a partial segment loss regarded as a loss sampling to learn integral action parts from labeled segments.
  arXiv  Detail & Related papers  (2020-07-03T10:32:19Z)

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.