Deep learning based neuronal soma detection and counting for Alzheimer's disease analysis

Li, Qiufu; Zhang, Yu; Liang, Hanbang; Gong, Hui; Jiang, Liang; Liu, Qiong; Shen, Linlin

doi:10.1016/j.cmpb.2021.106023

Cited by 13 publications

(7 citation statements)

References 40 publications

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“… 26 Moreover, deep learning techniques have been explored in the field of neuroscience, including neuron image segmentation, 27 neural stem cell differentiation, 28 and neuronal soma detection. 29 However, it is important to note that there is currently a lack of research specifically focusing on biomedical cell detection in the context of lumbar foraminal stenosis.…”

Section: Discussionmentioning

confidence: 99%

Development of a Deep Learning Model for the Analysis of Dorsal Root Ganglion Chromatolysis in Rat Spinal Stenosis

Li,

Zheng,

Koh

et al. 2024

JPR

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Objective To create a deep learning (DL) model that can accurately detect and classify three distinct types of rat dorsal root ganglion neurons: normal, segmental chromatolysis, and central chromatolysis. The DL model has the potential to improve the efficiency and precision of neuron classification in research related to spinal injuries and diseases. Methods H&E slide images were divided into an internal training set (80%) and a test set (20%). The training dataset was labeled by two pathologists using pre-defined grades. Using this dataset, a two-component DL model was developed with the first component being a convolutional neural network (CNN) that was trained to detect the region of interest (ROI) and the second component being another CNN used for classification. Results A total of 240 lumbar dorsal root ganglion (DRG) pathology slide images from rats were analyzed. The internal testing results showed an accuracy of 93.13%, and the external dataset testing demonstrated an accuracy of 93.44%. Conclusion The DL model demonstrated a level of agreement comparable to that of pathologists in detecting and classifying normal and segmental chromatolysis neurons, although its agreement was slightly lower for central chromatolysis neurons. Significance: DL in improving the accuracy and efficiency of pathological analysis suggests that it may have a role in enhancing medical decision-making.

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Section: Discussionmentioning

confidence: 99%

Development of a Deep Learning Model for the Analysis of Dorsal Root Ganglion Chromatolysis in Rat Spinal Stenosis

Li,

Zheng,

Koh

et al. 2024

JPR

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“…Deep neural networks play an increasingly important role in the staging, 30 detection, 31 characterization, 32 segmentation, 33 classification, 34 and computer-aided diagnosis [35][36][37][38][39][40][41] of disease on imaging. However, optimal integration with clinical data -which is often sparsely encoded and low-dimensional -requires sophisticated data fusion techniques.…”

Section: Discussionmentioning

confidence: 99%

Towards optimal deep fusion of imaging and clinical data via a model‐based description of fusion quality

Wang

Konanur

et al. 2023

Medical Physics

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Background: Due to intrinsic differences in data formatting, data structure, and underlying semantic information, the integration of imaging data with clinical data can be non-trivial. Optimal integration requires robust data fusion, that is, the process of integrating multiple data sources to produce more useful information than captured by individual data sources. Here, we introduce the concept of fusion quality for deep learning problems involving imaging and clinical data. We first provide a general theoretical framework and numerical validation of our technique. To demonstrate real-world applicability, we then apply our technique to optimize the fusion of CT imaging and hepatic blood markers to estimate portal venous hypertension, which is linked to prognosis in patients with cirrhosis of the liver. Purpose: To develop a measurement method of optimal data fusion quality deep learning problems utilizing both imaging data and clinical data. Methods: Our approach is based on modeling the fully connected layer (FCL) of a convolutional neural network (CNN) as a potential function, whose distribution takes the form of the classical Gibbs measure. The features of the FCL are then modeled as random variables governed by state functions, which are interpreted as the different data sources to be fused. The probability density of each source, relative to the probability density of the FCL, represents a quantitative measure of source-bias. To minimize this source-bias and optimize CNN performance, we implement a vector-growing encoding scheme called positional encoding, where low-dimensional clinical data are transcribed into a rich feature space that complements high-dimensional imaging features. We first provide a numerical validation of our approach based on simulated Gaussian processes. We then applied our approach to patient data, where we optimized the fusion of CT images with blood markers to predict portal venous hypertension in patients with cirrhosis of the liver. This patient study was based on a modified ResNet-152 model that incorporates both images and blood markers as input.These two data sources were processed in parallel, fused into a single FCL, and optimized based on our fusion quality framework. Results: Numerical validation of our approach confirmed that the probability density function of a fused feature space converges to a source-specific probability density function when source data are improperly fused. Our numerical results demonstrate that this phenomenon can be quantified as a measure of fusion quality. On patient data, the fused model consisting of both imaging data and positionally encoded blood markers at the theoretically optimal fusion quality metric achieved an AUC of 0.74 and an accuracy of 0.71. This model was statistically better than the imaging-only model (AUC = 0.60; accuracy = 0.62), 3526

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“…However, the majority of these methods were developed for brain neurons, and are not well generalizable to RGC images. 10 – 14 …”

Section: Introductionmentioning

confidence: 99%

RGC-Net: An Automatic Reconstruction and Quantification Algorithm for Retinal Ganglion Cells Based on Deep Learning

Hao

Tao

et al. 2023

Trans. Vis. Sci. Tech.

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Purpose The purpose of this study was to develop a deep learning-based fully automated reconstruction and quantification algorithm which automatically delineates the neurites and somas of retinal ganglion cells (RGCs). Methods We trained a deep learning-based multi-task image segmentation model, RGC-Net, that automatically segments the neurites and somas in RGC images. A total of 166 RGC scans with manual annotations from human experts were used to develop this model, whereas 132 scans were used for training, and the remaining 34 scans were reserved as testing data. Post-processing techniques removed speckles or dead cells in soma segmentation results to further improve the robustness of the model. Quantification analyses were also conducted to compare five different metrics obtained by our automated algorithm and manual annotations. Results Quantitatively, our segmentation model achieves average foreground accuracy, background accuracy, overall accuracy, and dice similarity coefficient of 0.692, 0.999, 0.997, and 0.691 for the neurite segmentation task, and 0.865, 0.999, 0.997, and 0.850 for the soma segmentation task, respectively. Conclusions The experimental results demonstrate that RGC-Net can accurately and reliably reconstruct neurites and somas in RGC images. We also demonstrate our algorithm is comparable to human manually curated annotations in quantification analyses. Translational Relevance Our deep learning model provides a new tool that can trace and analyze the RGC neurites and somas efficiently and faster than manual analysis.

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Deep learning based neuronal soma detection and counting for Alzheimer's disease analysis

Cited by 13 publications

References 40 publications

Development of a Deep Learning Model for the Analysis of Dorsal Root Ganglion Chromatolysis in Rat Spinal Stenosis

Development of a Deep Learning Model for the Analysis of Dorsal Root Ganglion Chromatolysis in Rat Spinal Stenosis

Towards optimal deep fusion of imaging and clinical data via a model‐based description of fusion quality

RGC-Net: An Automatic Reconstruction and Quantification Algorithm for Retinal Ganglion Cells Based on Deep Learning

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