Advanced Image Segmentation Techniques for Neural Activity Detection via C-fos Immediate Early Gene Expression

• URL: http://arxiv.org/abs/2312.08177v1
• Date: Wed, 13 Dec 2023 14:36:16 GMT
• Title: Advanced Image Segmentation Techniques for Neural Activity Detection via C-fos Immediate Early Gene Expression
• Authors: Peilin Cai
• Abstract summary: We develop a novel workflow for the segmentation process involving Convolutional Neural Networks (CNNs) and the Unet model. We demonstrate the effectiveness of our method in distinguishing areas with significant C-fos expression from normal tissue areas.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper investigates the application of advanced image segmentation techniques to analyze C-fos immediate early gene expression, a crucial marker for neural activity. Due to the complexity and high variability of neural circuits, accurate segmentation of C-fos images is paramount for the development of new insights into neural function. Amidst this backdrop, this research aims to improve accuracy and minimize manual intervention in C-fos image segmentation by leveraging the capabilities of CNNs and the Unet model. We describe the development of a novel workflow for the segmentation process involving Convolutional Neural Networks (CNNs) and the Unet model, demonstrating their efficiency in various image segmentation tasks. Our workflow incorporates pre-processing steps such as cropping, image feature extraction, and clustering for the training dataset selection. We used an AutoEncoder model to extract features and implement constrained clustering to identify similarities and differences in image types. Additionally, we utilized manual and automatic labeling approaches to enhance the performance of our model. We demonstrated the effectiveness of our method in distinguishing areas with significant C-fos expression from normal tissue areas. Lastly, we implemented a modified Unet network for the detection of C-fos expressions. This research contributes to the development of more efficient and automated image segmentation methods, advancing the understanding of neural function in neuroscience research.

Related papers

• Research on Optimization of Natural Language Processing Model Based on Multimodal Deep Learning [0.036651088217486416]
  This project intends to study the image representation based on attention mechanism and multimodal data. By adding multiple pattern layers to the model, the semantic and hidden layers of image content are integrated. The word vector is quantified by the Word2Vec method and then evaluated by a word embedding convolutional neural network.
  arXiv  Detail & Related papers  (2024-06-13T06:03:59Z)
• Convolutional neural network based on sparse graph attention mechanism for MRI super-resolution [0.34410212782758043]
  Medical image super-resolution (SR) reconstruction using deep learning techniques can enhance lesion analysis and assist doctors in improving diagnostic efficiency and accuracy. Existing deep learning-based SR methods rely on convolutional neural networks (CNNs), which inherently limit the expressive capabilities of these models. We propose an A-network that utilizes multiple convolution operator feature extraction modules (MCO) for extracting image features.
  arXiv  Detail & Related papers  (2023-05-29T06:14:22Z)
• Convolutional Neural Generative Coding: Scaling Predictive Coding to Natural Images [79.07468367923619]
  We develop convolutional neural generative coding (Conv-NGC) We implement a flexible neurobiologically-motivated algorithm that progressively refines latent state maps. We study the effectiveness of our brain-inspired neural system on the tasks of reconstruction and image denoising.
  arXiv  Detail & Related papers  (2022-11-22T06:42:41Z)
• Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image Analysis [48.02011627390706]
  We develop a novel self-supervised approach that occludes targeted regions to improve the pre-training procedure. We show that training the agent against the prediction model can significantly improve the semantic features extracted for downstream classification tasks.
  arXiv  Detail & Related papers  (2022-03-25T19:05:06Z)
• End-to-end Neuron Instance Segmentation based on Weakly Supervised Efficient UNet and Morphological Post-processing [0.0]
  We present an end-to-end weakly-supervised framework to automatically detect and segment NeuN stained neuronal cells on histological images. We integrate the state-of-the-art network, EfficientNet, into our U-Net-like architecture.
  arXiv  Detail & Related papers  (2022-02-17T14:35:45Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Learning Fuzzy Clustering for SPECT/CT Segmentation via Convolutional Neural Networks [5.3123694982708365]
  Quantitative bone single-photon emission computed tomography (QBSPECT) has the potential to provide a better quantitative assessment of bone metastasis than planar bone scintigraphy. The segmentation of anatomical regions-of-interests (ROIs) still relies heavily on the manual delineation by experts. This work proposes a fast and robust automated segmentation method for partitioning a QBSPECT image into lesion, bone, and background.
  arXiv  Detail & Related papers  (2021-04-17T19:03:52Z)
• Sparse Signal Models for Data Augmentation in Deep Learning ATR [0.8999056386710496]
  We propose a data augmentation approach to incorporate domain knowledge and improve the generalization power of a data-intensive learning algorithm. We exploit the sparsity of the scattering centers in the spatial domain and the smoothly-varying structure of the scattering coefficients in the azimuthal domain to solve the ill-posed problem of over-parametrized model fitting.
  arXiv  Detail & Related papers  (2020-12-16T21:46:33Z)
• 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)
• Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting [86.33696045574692]
  We propose a Cycle Consistency Panoptic Domain Adaptive Mask R-CNN (CyC-PDAM) architecture for unsupervised nuclei segmentation in histopathology images. We first propose a nuclei inpainting mechanism to remove the auxiliary generated objects in the synthesized images. Secondly, a semantic branch with a domain discriminator is designed to achieve panoptic-level domain adaptation.
  arXiv  Detail & Related papers  (2020-05-05T11:08:26Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.