Deep learning techniques in liver tumour diagnosis using CT and MR imaging - A systematic review

Priya, Lakshmi; Pottakkat, Biju; Ramkumar, G.

doi:10.1016/j.artmed.2023.102557

Cited by 12 publications

(4 citation statements)

References 93 publications

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“…Системы автоматизированного проектирования с применением глубокого обучения разрабатываются для тщательного анализа изображений в различных областях медицины [7]. Все больше фактов демонстрируют возможность применения глубокого обучения в обработке изображений магнитно-резонансной и компьютерной томографии [8][9][10][11], высокой точности обнаружения полипов при колоноскопии [12,13], диагностике РМП по данным цистоскопии [14].…”

Section: список сокращенийunclassified

Deep learning for early detection of papillary bladder cancer on a limited set of cystoscopic images

Rozova,

Russo,

Lekarev

et al. 2024

jour

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Aim. The aim of this study was to develop and evaluate the effectiveness of a convolutional neural network (CNN) in detecting papillary bladder cancer (PBC) using a limited set of cystoscopic images.Materials and methods. Twenty patients who underwent white light cystoscopy and histologically confirmed papillary bladder cancer were included in the study. The dataset included 125 images retrieved and marked by a urologist: 88 images were papillary tumors and 37 were healthy bladder wall tissue. 100 images were selected for training and 25 images were selected for validation. The U-net architecture and the CNN VGG16 model were used. A binary mask was manually created for each image based on the comments given by the urologist. Each image was additionally processed for model compatibility, with 224×224 pixel images as input to reduce the number of parameters. The dataset was augmented by applying vertical and horizontal turns, as well as random rotations. The following metrics were calculated: Dice coefficient, sensitivity, specificity, proportion of false positives and false negatives, accuracy, and area under the ROC curve.Results. The original data set yielded the following parameters: specificity 84.56%, sensitivity 82.18%, false positive rate 15.44%, false negative rate 17.82%, accuracy 76.40%, and a Dice coefficient 83.16%. For the augmented dataset, the following values were obtained: specificity: 82.99%, sensitivity: 82.70%, false positive rate 17.01%, false negative rate 17.30%, accuracy 74.72%, Dice coefficient – 82.82%. The area under the ROC curves was 92.93% for the original dataset and 91.69% for the augmented dataset.Conclusion. The CNN created in this study can detect signs of early PBC when analyzing cystoscopic images. The results of the study can be a starting point for developing new methods to diagnose PBC using deep learning technologies.

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Section: список сокращенийunclassified

Deep learning for early detection of papillary bladder cancer on a limited set of cystoscopic images

Rozova,

Russo,

Lekarev

et al. 2024

jour

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“…The U-shaped convolutional neural network architecture (U-net), beneficial from its generalizability and transferability, has been widely applied to specific medical image segmentation tasks (Çiçek et al 2016(Çiçek et al , Liu et al 2020. Many studies have effectively employed the U-net architecture to automatic liver tumor segmentation and most of them have focused on CT images (Jin et al 2020, LI et al 2020, Meng et al 2021, Tran et al 2021, usually obtained from publicly available datasets such as LiTS and 3DIRCADb (Lakshmipriya et al 2023, Syed Inthiyaz 2023. However, DCE MRI-based deep learning techniques for HCC remains scarce compared to those on CT images.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of hepatocellular carcinoma on dynamic contrast-enhanced MRI based on deep learning

Luo,

Li,

Chen

et al. 2024

Phys. Med. Biol.

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Objective: Precise hepatocellular carcinoma (HCC) detection is crucial for clinical management. While studies focus on CT-based automatic algorithms, there is a rareness of research on automatic detection based on dynamic contrast enhanced (DCE) MRI. This study is to develop an automatic detection and segmentation deep learning model for HCC using DCE. Approach: DCE images acquired from 2016 to 2021 were retrospectively collected. Then, 382 patients (301 male; 81 female) with 466 lesions pathologically confirmed were included and divided into an 80% training-validation set and a 20% independent test set. For external validation, 51 patients (42 male; 9 female) in another hospital from 2018 to 2021 were included. The U-net architecture was modified to accommodate multi-phasic DCE input. The model was trained with the training-validation set using five-fold cross-validation, and furtherly evaluated with the independent test set using comprehensive metrics for segmentation and detection performance. The proposed automatic segmentation model consisted of five main steps: phase registration, automatic liver region extraction using a pre-trained model, automatic HCC lesion segmentation using the multi-phasic deep learning model, ensemble of five-fold predictions, and post-processing using connected component analysis to enhance the performance to refine predictions and eliminate false positives. Main results: The proposed model achieved a mean dice similarity coefficient (DSC) of 0.81±0.11, a sensitivity of 94.41±15.50%, a precision of 94.19±17.32%, and 0.14±0.48 false positive lesions per patient in the independent test set. The model detected 88% (80/91) HCC lesions in the condition of DSC>0.5, and the DSC per tumor was 0.80±0.13. In the external set, the model detected 92% (58/62) lesions with 0.12±0.33 false positives per patient, and the DSC per tumor was 0.75±0.10. Significance: This study developed an automatic detection and segmentation deep learning model for HCC using DCE, which yielded promising post-processed results in accurately identifying and delineating HCC lesions.

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“…Artificial intelligence is frequently used in many fields, from health to robotics. To illustrate, a liver tumor diagnosis was realized using deep learning techniques and CT -MR imaging [6]. In another study, an industrial robot was converted into a collaborative robot that did not harm people by utilizing deep learning techniques [7].…”

Section: Introductionmentioning

confidence: 99%

Layer Selection for Subtraction and Concatenation: A Method for Visual Velocity Estimation of a Mobile Robot

Bıngol

2024

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Kinematic information such as position, velocity, and acceleration is critical to determine the three-dimensional state of the robot in space. In this study, it is aimed to estimate as visual the linear and angular velocity of a mobile robot. Additionally, another aim of this study is to determine the suitability of the concatenation or subtraction layer in the Convolutional Neural Network (CNN) that will make this estimate. For these purposes, first, a simulation environment was created. 9000 pairs of images and necessary velocity information were collected from this simulation environment for training. Similarly, 1000 pairs of images and velocity information were gathered for validation. Four different CNN models were designed and these models were trained and tested using these datasets. As a result of the test, the lowest average error for linear velocity estimation was calculated as 0.93e-3m/s and angular velocity estimation was measured as 4.37e-3rad/s. It was observed that the results were sufficient for linear and angular velocity prediction according to statistical analysis of errors. In addition, it was observed that the subtraction layer can be used instead of the concatenation layer in the CNN architectures for hardware-limited systems. As a result, visual velocity estimation of mobile robots has been achieved with this study and the framework of CNN models has been drawn for this problem.

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Deep learning techniques in liver tumour diagnosis using CT and MR imaging - A systematic review

Cited by 12 publications

References 93 publications

Deep learning for early detection of papillary bladder cancer on a limited set of cystoscopic images

Deep learning for early detection of papillary bladder cancer on a limited set of cystoscopic images

Automatic segmentation of hepatocellular carcinoma on dynamic contrast-enhanced MRI based on deep learning

Layer Selection for Subtraction and Concatenation: A Method for Visual Velocity Estimation of a Mobile Robot

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