Applications of Deep Learning in Medical Imaging

Maitra, Sanjit; Ghosh, Ratul; Ghosh, Kuntal

doi:10.1007/978-3-030-11479-4_6

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…This would expand the scope of the network and improve the accuracy of calculations in the new range. Machine learning and artificial intelligence in the field of medical physics often lends itself to imaging studies with deep convolutional neural networks (Maier et al 2019, Maitra et al 2019, Nensa et al 2019. This study serves as a first look into applying machine learning techniques into the field of alpha-particle microdosimetry.…”

Section: Discussionmentioning

confidence: 99%

Alpha particle microdosimetry calculations using a shallow neural network

Wagstaff¹,

Gabiña²,

Mínguez³

et al. 2022

Phys. Med. Biol.

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A shallow neural network was trained to accurately calculate the microdosimetric parameters, <z1> and <z1 2> (the first and second moments of the single-event specific energy spectra, respectively) for use in alpha-particle microdosimetry calculations. The regression network of four inputs and two outputs was created in MATLAB and trained on a data set consisting of both previously published microdosimetric data and recent Monte Carlo simulations. The input data consisted of the alpha-particle energies (3.97–8.78 MeV), cell nuclei radii (2–10 µm), cell radii (2.5–20 µm), and eight different source-target configurations. These configurations included both single cells in suspension and cells in geometric clusters. The mean square error (MSE) was used to measure the performance of the network. The sizes of the hidden layers were chosen to minimize MSE without overfitting. The final neural network consisted of two hidden layers with 13 and 20 nodes, respectively, each with tangential sigmoid transfer functions, and was trained on 1932 data points. The overall training/validation resulted in a MSE = 3.71×10-7. A separate testing data set included input values that were not seen by the trained network. The final test on 892 separate data points resulted in a MSE = 2.80×10-7. The 95th percentile testing data errors were within ±1.4% for <z1> outputs and ±2.8% for <z1 2> outputs, respectively. Cell survival was also predicted using actual vs. neural network generated microdosimetric moments and showed overall agreement within ±3.5%. In summary, this trained neural network can accurately produce microdosimetric parameters used for the study of alpha-particle emitters. The network can be exported and shared for tests on independent data sets and new calculations.

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Section: Discussionmentioning

confidence: 99%

Alpha particle microdosimetry calculations using a shallow neural network

Wagstaff¹,

Gabiña²,

Mínguez³

et al. 2022

Phys. Med. Biol.

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“…Convolutional neural networks (CNNs), notably effective in medical imaging analysis, have demonstrated superior capabilities compared to conventional methods [7], [8]. The integration of deep learning algorithms, specifically CNNs, has revolutionized medical image analysis across various domains, including neurology, cardiology, and pathology [9], [10]. Traditional diagnostic methods, although effective, have limitations in terms of subjectivity and time consumption.…”

Section: Introductionmentioning

confidence: 99%

Lumbar Disease Classification Using an Involutional Neural Based VGG Nets (INVGG)

Abuhayi,

Bezabh,

Ayalew

2024

IEEE Access

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Degenerative diseases of the lumbar spine, such as spondylolisthesis, disc degeneration, and lumbar spinal stenosis, are major contributors to global disability. Accurate classification of lumbar diseases is crucial for effective medical diagnosis. This paper introduces an innovative methodology for lumbar disease classification, addressing the limitations of traditional convolutional neural networks (CNNs). We propose a novel approach, InVGG, which combines involutional neural networks with the VGG architecture. Unlike traditional CNNs, InVGG utilizes involution kernels that are location-specific and channel-agnostic, enhancing its adaptability to varied visual patterns in medical images. Our study focuses on a four-class lumbar disease classification problem using sagittal T2 MRI images. The evaluation of InVGG is compared with traditional CNNs (VGG model) and machine learning algorithms, demonstrating superior performance in terms of accuracy, precision, recall, and AUC ROC values. InVGG achieves an impressive 96% accuracy on the testing set and 99% on the training set, showcasing its potential for accurate spinal lumbar disease classification. The reduced parameter count of InVGG compared to CNNs (VGG) makes it more resourceefficient, especially in scenarios with limited computational resources and datasets. The promising results position InVGG as a valuable tool for precise lumbar disease classification, with implications for improving patient care in resource-constrained scenarios.

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“…Recently, deep learning (DL) has revolutionized medical image analysis and achieved superior performance, involving use of deep neural networks to learn hierarchical feature representations from medical images, and eliminating the need for hand-designed features [23]. Deep learning models have been successfully applied in various medical imaging applications and have shown better capabilities in segmenting and classifying medical images, such as MRI and CT images, compared to conventional image processing and machine learning techniques [24, 25]. Several DL-based lung segmentation approaches have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Interactive Segmentation of Lung Tissue and Lung Excursion in Thoracic Dynamic MRI Based on Shape-guided Convolutional Neural Networks

Xie,

Udupa,

Tong

et al. 2024

Preprint

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Purpose: Lung tissue and lung excursion segmentation in thoracic dynamic magnetic resonance imaging (dMRI) is a critical step for quantitative analysis of thoracic structure and function in patients with respiratory disorders such as Thoracic Insufficiency Syndrome (TIS). However, the complex variability of intensity and shape of anatomical structures and the low contrast between the lung and surrounding tissue in MR images seriously hamper the accuracy and robustness of automatic segmentation methods. In this paper, we develop an interactive deep-learning based segmentation system to solve this problem. Material & Methods: Considering the significant difference in lung morphological characteristics between normal subjects and TIS subjects, we utilized two independent data sets of normal subjects and TIS subjects to train and test our model. 202 dMRI scans from 101 normal pediatric subjects and 92 dMRI scans from 46 TIS pediatric subjects were acquired for this study and were randomly divided into training, validation, and test sets by an approximate ratio of 5:1:4. First, we designed an interactive region of interest (ROI) strategy to detect the lung ROI in dMRI for accelerating the training speed and reducing the negative influence of tissue located far away from the lung on lung segmentation. Second, we utilized a modified 2D U-Net to segment the lung tissue in lung ROIs, in which the adjacent slices are utilized as the input data to take advantage of the spatial information of the lungs. Third, we extracted the lung shell from the lung segmentation results as the shape feature and inputted the lung ROIs with shape feature into another modified 2D U-Net to segment the lung excursion in dMRI. To evaluate the performance of our approach, we computed the Dice coefficient (DC) and max-mean Hausdorff distance (MM-HD) between manual and automatic segmentations. In addition, we utilized Coefficient of Variation (CV) to assess the variability of our method on repeated dMRI scans and the differences of lung tidal volumes computed from the manual and automatic segmentation results. Results: The proposed system yielded mean Dice coefficients of 0.96/0.02 and 0.89/0.05 for lung segmentation in dMRI of normal subjects and TIS subjects, respectively, demonstrating excellent agreement with manual delineation results. The Coefficient of Variation and p-values show that the estimated lung tidal volumes of our approach are statistically indistinguishable from those derived by manual segmentations. Conclusions: The proposed approach can be applied to lung tissue and lung excursion segmentation from dynamic MR images with high accuracy and efficiency. The proposed approach has the potential to be utilized in the assessment of patients with TIS via dMRI routinely.

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Applications of Deep Learning in Medical Imaging

Cited by 7 publications

References 18 publications

Alpha particle microdosimetry calculations using a shallow neural network

Alpha particle microdosimetry calculations using a shallow neural network

Lumbar Disease Classification Using an Involutional Neural Based VGG Nets (INVGG)

Interactive Segmentation of Lung Tissue and Lung Excursion in Thoracic Dynamic MRI Based on Shape-guided Convolutional Neural Networks

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