Desmoking laparoscopy surgery images using an image-to-image translation guided by an embedded dark channel

• URL: http://arxiv.org/abs/2004.08947v1
• Date: Sun, 19 Apr 2020 19:51:24 GMT
• Title: Desmoking laparoscopy surgery images using an image-to-image translation guided by an embedded dark channel
• Authors: Sebasti\'an Salazar-Colores, Hugo Alberto-Moreno, C\'esar Javier Ortiz-Echeverri, Gerardo Flores
• Abstract summary: In laparoscopic surgery, the visibility in the image can be severely degraded by the smoke caused by the $CO$ injection, and dissection tools. In this paper, a novel computational approach to remove the smoke effects is introduced. The proposed method is based on an image-to-image conditional generative adversarial network in which a dark channel is used as an embedded guide mask.
• Score: 3.1706553206969916
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In laparoscopic surgery, the visibility in the image can be severely degraded by the smoke caused by the $CO_2$ injection, and dissection tools, thus reducing the visibility of organs and tissues. This lack of visibility increases the surgery time and even the probability of mistakes conducted by the surgeon, then producing negative consequences on the patient's health. In this paper, a novel computational approach to remove the smoke effects is introduced. The proposed method is based on an image-to-image conditional generative adversarial network in which a dark channel is used as an embedded guide mask. Obtained experimental results are evaluated and compared quantitatively with other desmoking and dehazing state-of-art methods using the metrics of the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index. Based on these metrics, it is found that the proposed method has improved performance compared to the state-of-the-art. Moreover, the processing time required by our method is 92 frames per second, and thus, it can be applied in a real-time medical system trough an embedded device.

Related papers

• Intraoperative Registration by Cross-Modal Inverse Neural Rendering [61.687068931599846]
  We present a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering. Our approach separates implicit neural representation into two components, handling anatomical structure preoperatively and appearance intraoperatively. We tested our method on retrospective patients' data from clinical cases, showing that our method outperforms state-of-the-art while meeting current clinical standards for registration.
  arXiv  Detail & Related papers  (2024-09-18T13:40:59Z)
• Rotational Augmented Noise2Inverse for Low-dose Computed Tomography Reconstruction [83.73429628413773]
  Supervised deep learning methods have shown the ability to remove noise in images but require accurate ground truth. We propose a novel self-supervised framework for LDCT, in which ground truth is not required for training the convolutional neural network (CNN) Numerical and experimental results show that the reconstruction accuracy of N2I with sparse views is degrading while the proposed rotational augmented Noise2Inverse (RAN2I) method keeps better image quality over a different range of sampling angles.
  arXiv  Detail & Related papers  (2023-12-19T22:40:51Z)
• Feature-oriented Deep Learning Framework for Pulmonary Cone-beam CT (CBCT) Enhancement with Multi-task Customized Perceptual Loss [9.59233136691378]
  Cone-beam computed tomography (CBCT) is routinely collected during image-guided radiation therapy. Recent deep learning-based CBCT enhancement methods have shown promising results in suppressing artifacts. We propose a novel feature-oriented deep learning framework that translates low-quality CBCT images into high-quality CT-like imaging.
  arXiv  Detail & Related papers  (2023-11-01T10:09:01Z)
• OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing [4.951748109810726]
  Optimal retinal image quality is mandated for accurate medical diagnoses and automated analyses. We propose an unpaired image-to-image translation scheme for mapping low-quality retinal CFPs to high-quality counterparts. We validated the integrated framework, OTRE, on three publicly available retinal image datasets.
  arXiv  Detail & Related papers  (2023-02-06T18:39:40Z)
• Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
  We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T08:41:17Z)
• Unsupervised Denoising of Retinal OCT with Diffusion Probabilistic Model [0.2578242050187029]
  We present a diffusion probabilistic model that is fully unsupervised to learn from noise instead of signal. Our method can significantly improve the image quality with a simple working pipeline and a small amount of training data.
  arXiv  Detail & Related papers  (2022-01-27T19:02:38Z)
• 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)
• Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-Contrast CT Images [50.55978219682419]
  We propose a symmetry enhanced attention network (SEAN) for acute ischemic infarct segmentation. Our proposed network automatically transforms an input CT image into the standard space where the brain tissue is bilaterally symmetric. The proposed SEAN outperforms some symmetry-based state-of-the-art methods in terms of both dice coefficient and infarct localization.
  arXiv  Detail & Related papers  (2021-10-11T07:13:26Z)
• Retinal OCT Denoising with Pseudo-Multimodal Fusion Network [0.41998444721319206]
  We propose a learning-based method that exploits information from the single-frame noisy B-scan and a pseudo-modality that is created with the aid of the self-fusion method. Our method can effectively suppress the speckle noise and enhance the contrast between retina layers while the overall structure and small blood vessels are preserved.
  arXiv  Detail & Related papers  (2021-07-09T08:00:20Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• A CNN-Based Blind Denoising Method for Endoscopic Images [19.373025463383385]
  Many low-quality endoscopic images exist due to limited illumination and complex environment in GI tract. This paper proposes a convolutional blind denoising network for endoscopic images.
  arXiv  Detail & Related papers  (2020-03-16T03:11:11Z)

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.