Hybrid Deep Learning-Based for Enhanced Occlusion Segmentation in PICU Patient Monitoring

• URL: http://arxiv.org/abs/2407.13341v1
• Date: Thu, 18 Jul 2024 09:37:55 GMT
• Title: Hybrid Deep Learning-Based for Enhanced Occlusion Segmentation in PICU Patient Monitoring
• Authors: Mario Francisco Munoz, Hoang Vu Huy, Thanh-Dung Le,
• Abstract summary: We propose a hybrid approach to segment common occlusions encountered in remote monitoring applications within PICUs. Our approach centers on creating a deep-learning pipeline for limited training data scenarios. The proposed framework yields an overall classification performance with 92.5% accuracy, 93.8% recall, 90.3% precision, and 92.0% F1-score.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Remote patient monitoring has emerged as a prominent non-invasive method, using digital technologies and computer vision (CV) to replace traditional invasive monitoring. While neonatal and pediatric departments embrace this approach, Pediatric Intensive Care Units (PICUs) face the challenge of occlusions hindering accurate image analysis and interpretation. \textit{Objective}: In this study, we propose a hybrid approach to effectively segment common occlusions encountered in remote monitoring applications within PICUs. Our approach centers on creating a deep-learning pipeline for limited training data scenarios. \textit{Methods}: First, a combination of the well-established Google DeepLabV3+ segmentation model with the transformer-based Segment Anything Model (SAM) is devised for occlusion segmentation mask proposal and refinement. We then train and validate this pipeline using a small dataset acquired from real-world PICU settings with a Microsoft Kinect camera, achieving an Intersection-over-Union (IoU) metric of 85\%. \textit{Results}: Both quantitative and qualitative analyses underscore the effectiveness of our proposed method. The proposed framework yields an overall classification performance with 92.5\% accuracy, 93.8\% recall, 90.3\% precision, and 92.0\% F1-score. Consequently, the proposed method consistently improves the predictions across all metrics, with an average of 2.75\% gain in performance compared to the baseline CNN-based framework. \textit{Conclusions}: Our proposed hybrid approach significantly enhances the segmentation of occlusions in remote patient monitoring within PICU settings. This advancement contributes to improving the quality of care for pediatric patients, addressing a critical need in clinical practice by ensuring more accurate and reliable remote monitoring.

Related papers

• Goal-conditioned reinforcement learning for ultrasound navigation guidance [4.648318344224063]
  We propose a novel ultrasound navigation assistance method based on contrastive learning as goal-conditioned reinforcement learning (G) We augment the previous framework using a novel contrastive patient method (CPB) and a data-augmented contrastive loss. Our method was developed with a large dataset of 789 patients and obtained an average error of 6.56 mm in position and 9.36 degrees in angle.
  arXiv  Detail & Related papers  (2024-05-02T16:01:58Z)
• Augmentation is AUtO-Net: Augmentation-Driven Contrastive Multiview Learning for Medical Image Segmentation [3.1002416427168304]
  This thesis focuses on retinal blood vessel segmentation tasks. It provides an extensive literature review of deep learning-based medical image segmentation approaches. It proposes a novel efficient, simple multiview learning framework.
  arXiv  Detail & Related papers  (2023-11-02T06:31:08Z)
• SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis [3.1542695050861544]
  Deep learning-based supervised methods are widely popular in medical image analysis. They require a large amount of training data and face issues in generalisability to unseen datasets. We propose to explore patch-level instance-group discrimination and penalisation of inter-class variation using additive angular margin.
  arXiv  Detail & Related papers  (2023-05-31T21:28:08Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Segmentation of Bruch's Membrane in retinal OCT with AMD using anatomical priors and uncertainty quantification [4.5206601127476445]
  We propose an end-to-end deep learning method for automated Bruch's membrane (BM) segmentation in AMD patients. An Attention U-Net is trained to output a probability density function of the BM position, while taking into account the natural curvature of the surface. Besides the surface position, the method also estimates an A-scan wise uncertainty measure of the segmentation output.
  arXiv  Detail & Related papers  (2022-10-26T15:49:07Z)
• 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)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)
• Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network [51.44506007844284]
  We propose a shape-aware relation network for accurate and real-time landmark detection in endoscopic submucosal dissection surgery. We first devise an algorithm to automatically generate relation keypoint heatmaps, which intuitively represent the prior knowledge of spatial relations among landmarks. We then develop two complementary regularization schemes to progressively incorporate the prior knowledge into the training process.
  arXiv  Detail & Related papers  (2021-11-08T07:57:30Z)
• Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application [48.89674088331313]
  We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task. Our method is integrated into the radiosurgery treatment process and directly impacts the clinical workflow.
  arXiv  Detail & Related papers  (2021-08-21T16:15:40Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)

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.