Synthetic Generation of Three-Dimensional Cancer Cell Models from Histopathological Images

• URL: http://arxiv.org/abs/2101.11600v2
• Date: Mon, 8 Feb 2021 07:22:31 GMT
• Title: Synthetic Generation of Three-Dimensional Cancer Cell Models from Histopathological Images
• Authors: Yoav Alon and Xiang Yu and Huiyu Zhou
• Abstract summary: We propose a novel framework to generate synthetic three-dimensional histological models based on a generator-discriminator pattern. The proposed algorithms achieves high quantitative and qualitative synthesis evident in comparative evaluation metrics such as a low Frechet-Inception scores.
• Score: 19.778965983551114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthetic generation of three-dimensional cell models from histopathological images aims to enhance understanding of cell mutation, and progression of cancer, necessary for clinical assessment and optimal treatment. Classical reconstruction algorithms based on image registration of consecutive slides of stained tissues are prone to errors and often not suitable for the training of three-dimensional segmentation algorithms. We propose a novel framework to generate synthetic three-dimensional histological models based on a generator-discriminator pattern optimizing constrained features that construct a 3D model via a Blender interface exploiting smooth shape continuity typical for biological specimens. To capture the spatial context of entire cell clusters we deploy a novel deep topology transformer that implements and attention mechanism on cell group images to extract features for the frozen feature decoder. The proposed algorithms achieves high quantitative and qualitative synthesis evident in comparative evaluation metrics such as a low Frechet-Inception scores.

Related papers

• Single-shot reconstruction of three-dimensional morphology of biological cells in digital holographic microscopy using a physics-driven neural network [6.49455647840014]
  We propose a novel deep learning model, named MorpHoloNet, for single-shot of reconstruction 3D morphology. MorpHoloNet is optimized by minimizing the loss between the simulated and input holograms on the sensor plane. It enables direct reconstruction of 3D complex light field and 3D morphology of a test sample from its single-shot hologram.
  arXiv  Detail & Related papers  (2024-09-30T07:15:36Z)
• Zero123-6D: Zero-shot Novel View Synthesis for RGB Category-level 6D Pose Estimation [66.3814684757376]
  This work presents Zero123-6D, the first work to demonstrate the utility of Diffusion Model-based novel-view-synthesizers in enhancing RGB 6D pose estimation at category-level. The outlined method shows reduction in data requirements, removal of the necessity of depth information in zero-shot category-level 6D pose estimation task, and increased performance, quantitatively demonstrated through experiments on the CO3D dataset.
  arXiv  Detail & Related papers  (2024-03-21T10:38:18Z)
• Three-dimensional numerical schemes for the segmentation of the psoas muscle in X-ray computed tomography images [0.0]
  The analysis of the psoas muscle in morphological and functional imaging has proved to be an accurate approach to assess sarcopenia. The present study utilizes three-dimensional numerical schemes for psoas segmentation in low-dose X-ray computed tomography images.
  arXiv  Detail & Related papers  (2023-12-10T13:37:39Z)
• SynCLay: Interactive Synthesis of Histology Images from Bespoke Cellular Layouts [0.5249805590164901]
  We propose a novel framework called SynCLay that can construct realistic and high-quality histology images. We train SynCLay in an adversarial manner and integrate a nuclear segmentation and classification model. We show that augmenting limited real data with the synthetic data generated by our framework can significantly boost prediction performance of the cellular composition prediction task.
  arXiv  Detail & Related papers  (2022-12-28T11:07:00Z)
• Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANs [0.5039813366558306]
  We propose a method for generating labeled data for the application of supervised deep-learning (DL) training. The image synthesis consists of label deformation and label-to-image translation tasks. We demonstrate that such an approach could provide a solution to diversify and enrich an available database of cardiac MR images.
  arXiv  Detail & Related papers  (2022-09-09T10:17:49Z)
• OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing [62.993663757843464]
  Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion. OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues. No standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
  arXiv  Detail & Related papers  (2022-06-17T08:11:26Z)
• Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis [65.47507533905188]
  Conditional generative adversarial networks have been applied to generate synthetic histopathology images. We propose a sharpness loss regularized generative adversarial network to synthesize realistic histopathology images.
  arXiv  Detail & Related papers  (2021-10-27T18:54:25Z)
• Free-form tumor synthesis in computed tomography images via richer generative adversarial network [25.20811195237978]
  We propose a new richer generative adversarial network for free-form 3D tumor/lesion synthesis in computed tomography (CT) images. The network is composed of a new richer convolutional feature enhanced dilated-gated generator (RicherDG) and a hybrid loss function.
  arXiv  Detail & Related papers  (2021-04-20T00:49:35Z)
• Data-driven generation of plausible tissue geometries for realistic photoacoustic image synthesis [53.65837038435433]
  Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties. We propose a novel approach to PAT data simulation, which we refer to as "learning to simulate" We leverage the concept of Generative Adversarial Networks (GANs) trained on semantically annotated medical imaging data to generate plausible tissue geometries.
  arXiv  Detail & Related papers  (2021-03-29T11:30:18Z)
• Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN [52.851990439671475]
  We propose a novel end-to-end GAN architecture that can generate high-resolution 3D images. We achieve this goal by using different configurations between training and inference. Experiments on 3D thorax CT and brain MRI demonstrate that our approach outperforms state of the art in image generation.
  arXiv  Detail & Related papers  (2020-08-05T02:33:04Z)
• Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning [68.9515120904028]
  Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem. Regularizing priors are necessary to reduce artifacts by improving the condition of such problems. We devised a recurrent neural network (RNN) architecture with a novel split-convolutional gated recurrent unit (SC-GRU) as the building block.
  arXiv  Detail & Related papers  (2020-07-21T11:48:22Z)

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.