Enhancing frozen histological section images using permanent-section-guided deep learning with nuclei attention

• URL: http://arxiv.org/abs/2411.06583v1
• Date: Sun, 10 Nov 2024 20:16:32 GMT
• Title: Enhancing frozen histological section images using permanent-section-guided deep learning with nuclei attention
• Authors: Elad Yoshai, Gil Goldinger, Miki Haifler, Natan T. Shaked,
• Abstract summary: We present a generative deep learning approach to enhance frozen section images by leveraging guidance from permanent sections. Our method places a strong emphasis on the nuclei region, which contains critical information in both frozen and permanent sections.
• Score: 0.0
• License:
• Abstract: In histological pathology, frozen sections are often used for rapid diagnosis during surgeries, as they can be produced within minutes. However, they suffer from artifacts and often lack crucial diagnostic details, particularly within the cell nuclei region. Permanent sections, on the other hand, contain more diagnostic detail but require a time-intensive preparation process. Here, we present a generative deep learning approach to enhance frozen section images by leveraging guidance from permanent sections. Our method places a strong emphasis on the nuclei region, which contains critical information in both frozen and permanent sections. Importantly, our approach avoids generating artificial data in blank regions, ensuring that the network only enhances existing features without introducing potentially unreliable information. We achieve this through a segmented attention network, incorporating nuclei-segmented images during training and adding an additional loss function to refine the nuclei details in the generated permanent images. We validated our method across various tissues, including kidney, breast, and colon. This approach significantly improves histological efficiency and diagnostic accuracy, enhancing frozen section images within seconds, and seamlessly integrating into existing laboratory workflows.

Related papers

• CTARR: A fast and robust method for identifying anatomical regions on CT images via atlas registration [0.09130220606101362]
  We introduce CTARR, a novel generic method for CT Anatomical Region Recognition. The method serves as a pre-processing step for any deep learning-based CT image analysis pipeline. Our proposed method is based on atlas registration and provides a fast and robust way to crop any anatomical region encoded as one or multiple bounding box(es) from any unlabeled CT scan.
  arXiv  Detail & Related papers  (2024-10-03T08:52:21Z)
• Real-time guidewire tracking and segmentation in intraoperative x-ray [52.51797358201872]
  We propose a two-stage deep learning framework for real-time guidewire segmentation and tracking. In the first stage, a Yolov5 detector is trained, using the original X-ray images as well as synthetic ones, to output the bounding boxes of possible target guidewires. In the second stage, a novel and efficient network is proposed to segment the guidewire in each detected bounding box.
  arXiv  Detail & Related papers  (2024-04-12T20:39:19Z)
• CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
  We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism. We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
  arXiv  Detail & Related papers  (2024-03-21T15:13:36Z)
• Super resolution of histopathological frozen sections via deep learning preserving tissue structure [0.0]
  We present a new approach to super resolution for histopathology frozen sections. Our deep-learning architecture focuses on learning the error between interpolated images and real images. In comparison to existing methods, we obtained significant improvements in terms of Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) Our approach has a great potential in providing more-rapid frozen-section imaging, with less scanning, while preserving the high resolution in the imaged sample.
  arXiv  Detail & Related papers  (2023-10-17T09:52:54Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• Nuclei Segmentation in Histopathology Images using Deep Learning with Local and Global Views [12.549900112862769]
  This paper proposes a deep learning-based approach for nuclei segmentation. It addresses the problem of misprediction in patch border areas. We use both local and global patches to predict the final segmentation map.
  arXiv  Detail & Related papers  (2021-12-07T21:25:38Z)
• Deep Learning-based Frozen Section to FFPE Translation [4.221354566366119]
  Frozen sectioning (FS) is the preparation method of choice for microscopic evaluation of tissues during surgical operations. FS is prone to introducing misleading artificial structures, such as nuclear ice crystals, compression, and cutting artefacts. We propose an artificial intelligence (AI) method that improves FS image quality by computationally transforming frozen-sectioned whole-slide images (FS-WSIs) into whole-slide FFPE-style images in minutes.
  arXiv  Detail & Related papers  (2021-07-25T11:32:57Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins [31.835275627382497]
  We provide a solution to segmentation of imperfect data through time based on temporal propagation and uncertainty estimation. We demonstrate the value of this approach over frame-by-frame segmentation and regular temporal propagation on data from human embryonic kidney cells.
  arXiv  Detail & Related papers  (2020-11-20T16:30:55Z)
• Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification [49.32653090178743]
  We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
  arXiv  Detail & Related papers  (2020-07-02T12:00:53Z)
• Towards a Complete Pipeline for Segmenting Nuclei in Feulgen-Stained Images [52.946144307741974]
  Cervical cancer is the second most common cancer type in women around the world. We present a complete pipeline for the segmentation of nuclei in Feulgen-stained images using Convolutional Neural Networks. We achieved an overall IoU of 0.78, showing the affordability of the approach of nuclei segmentation on Feulgen-stained images.
  arXiv  Detail & Related papers  (2020-02-19T18:14:57Z)

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.