Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning

• URL: http://arxiv.org/abs/2107.14476v1
• Date: Fri, 30 Jul 2021 07:59:45 GMT
• Title: Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning
• Authors: Hongxu Yang, Caifeng Shan, R. Arthur Bouwman, Lukas R. C. Dekker, Alexander F. Kolen and Peter H. N. de With
• Abstract summary: We propose a semi-supervised learning framework for instrument segmentation in 3D US. To achieve the SSL learning, a Dual-UNet is proposed to segment the instrument. Our proposed method achieves Dice score of about 68.6%-69.1% and the inference time of about 1 sec. per volume.
• Score: 62.13520959168732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical instrument segmentation in 3D ultrasound is essential for image-guided intervention. However, to train a successful deep neural network for instrument segmentation, a large number of labeled images are required, which is expensive and time-consuming to obtain. In this article, we propose a semi-supervised learning (SSL) framework for instrument segmentation in 3D US, which requires much less annotation effort than the existing methods. To achieve the SSL learning, a Dual-UNet is proposed to segment the instrument. The Dual-UNet leverages unlabeled data using a novel hybrid loss function, consisting of uncertainty and contextual constraints. Specifically, the uncertainty constraints leverage the uncertainty estimation of the predictions of the UNet, and therefore improve the unlabeled information for SSL training. In addition, contextual constraints exploit the contextual information of the training images, which are used as the complementary information for voxel-wise uncertainty estimation. Extensive experiments on multiple ex-vivo and in-vivo datasets show that our proposed method achieves Dice score of about 68.6%-69.1% and the inference time of about 1 sec. per volume. These results are better than the state-of-the-art SSL methods and the inference time is comparable to the supervised approaches.

Related papers

• Wound Tissue Segmentation in Diabetic Foot Ulcer Images Using Deep Learning: A Pilot Study [5.397013836968946]
  We have created a DFUTissue dataset for the research community to evaluate wound tissue segmentation algorithms. The dataset contains 110 images with tissues labeled by wound experts and 600 unlabeled images. Due to the limited amount of annotated data, our framework consists of both supervised learning (SL) and semi-supervised learning (SSL) phases.
  arXiv  Detail & Related papers  (2024-06-23T05:01:51Z)
• Enhancing Weakly Supervised 3D Medical Image Segmentation through Probabilistic-aware Learning [52.249748801637196]
  3D medical image segmentation is a challenging task with crucial implications for disease diagnosis and treatment planning. Recent advances in deep learning have significantly enhanced fully supervised medical image segmentation. We propose a novel probabilistic-aware weakly supervised learning pipeline, specifically designed for 3D medical imaging.
  arXiv  Detail & Related papers  (2024-03-05T00:46:53Z)
• 3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs [8.502838668378432]
  We propose a new longitudinal self-supervised learning method, 3DTINC, based on non-contrastive learning. It is designed to learn perturbation-invariant features for 3D optical coherence tomography ( OCT) volumes, using augmentations specifically designed for OCT. Our experiments show that this temporal information is crucial for predicting progression of retinal diseases, such as age-related macular degeneration (AMD)
  arXiv  Detail & Related papers  (2023-12-28T11:47:12Z)
• PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis [56.63327669853693]
  We propose to incorporate the task of pixel restoration for explicitly encoding more pixel-level information into high-level semantics. We also address the preservation of scale information, a powerful tool in aiding image understanding. The proposed unified SSL framework surpasses its self-supervised counterparts on various tasks.
  arXiv  Detail & Related papers  (2023-01-02T17:47:27Z)
• PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training [52.895952593202054]
  We propose a new semi-supervised adversarial method called Patch Confidence Adrial Training (PCA) for medical image segmentation. PCA learns the pixel structure and context information in each patch to get enough gradient feedback, which aids the discriminator in convergent to an optimal state. Our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation.
  arXiv  Detail & Related papers  (2022-07-24T07:45:47Z)
• PoissonSeg: Semi-Supervised Few-Shot Medical Image Segmentation via Poisson Learning [0.505645669728935]
  Few-shot Semantic (FSS) is a promising strategy for breaking the deadlock in deep learning. FSS model still requires sufficient pixel-level annotated classes for training to avoid overfitting. We propose a novel semi-supervised FSS framework for medical image segmentation.
  arXiv  Detail & Related papers  (2021-08-26T10:24:04Z)
• Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet [74.22397862400177]
  We propose a novel catheter segmentation approach, which requests fewer annotations than the supervised learning method. Our scheme considers a deep Q learning as the pre-localization step, which avoids voxel-level annotation. With the detected catheter, patch-based Dual-UNet is applied to segment the catheter in 3D volumetric data.
  arXiv  Detail & Related papers  (2020-06-25T21:10:04Z)
• Contrastive learning of global and local features for medical image segmentation with limited annotations [10.238403787504756]
  A key requirement for the success of supervised deep learning is a large labeled dataset. We propose strategies for extending the contrastive learning framework for segmentation of medical images in the semi-supervised setting. In the limited annotation setting, the proposed method yields substantial improvements compared to other self-supervision and semi-supervised learning techniques.
  arXiv  Detail & Related papers  (2020-06-18T13:31:26Z)
• Robust Medical Instrument Segmentation Challenge 2019 [56.148440125599905]
  Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. Our challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap.
  arXiv  Detail & Related papers  (2020-03-23T14:35:08Z)

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.