Practical utility of liver segmentation methods in clinical surgeries and interventions

Fractional differentiation based image enhancement for automatic detection of malignant melanoma

Recent improvements in artificial intelligence and computer vision make it possible to automatically detect abnormalities in medical images. Skin lesions are one broad class of them. There are types of lesions that cause skin cancer, again with several types. Melanoma is one of the deadliest types of skin cancer. Its early diagnosis is at utmost importance. The treatments are greatly aided with artificial intelligence by the quick and precise diagnosis of these conditions. The identification and delineation of boundaries inside skin lesions have shown promise when using the basic image processing approaches for edge detection. Further enhancements regarding edge detections are possible. In this paper, the use of fractional differentiation for improved edge detection is explored on the application of skin lesion detection. A framework based on fractional differential filters for edge detection in skin lesion images is proposed that can improve automatic detection rate of malignant melanoma. The derived images are used to enhance the input images. Obtained images then undergo a classification process based on deep learning. A well-studied dataset of HAM10000 is used in the experiments. The system achieves 81.04% accuracy with EfficientNet model using the proposed fractional derivative based enhancements whereas accuracies are around 77.94% when using original images. In almost all the experiments, the enhanced images improved the accuracy. The results show that the proposed method improves the recognition performance.

A Review of Advancements and Challenges in Liver Segmentation

Liver segmentation technologies play vital roles in clinical diagnosis, disease monitoring, and surgical planning due to the complex anatomical structure and physiological functions of the liver. This paper provides a comprehensive review of the developments, challenges, and future directions in liver segmentation technology. We systematically analyzed high-quality research published between 2014 and 2024, focusing on liver segmentation methods, public datasets, and evaluation metrics. This review highlights the transition from manual to semi-automatic and fully automatic segmentation methods, describes the capabilities and limitations of available technologies, and provides future outlooks.

Dual-energy computed tomography with new virtual monoenergetic image reconstruction enhances prostate lesion image quality and improves the diagnostic efficacy for prostate cancer

BACKGROUND

Prostate cancer is one of the most common malignant tumors in middle-aged and elderly men and carries significant prognostic implications, and recent studies suggest that dual-energy computed tomography (DECT) utilizing new virtual monoenergetic images can enhance cancer detection rates. This study aimed to assess the impact of virtual monoenergetic images reconstructed from DECT arterial phase scans on the image quality of prostate lesions and their diagnostic performance for prostate cancer.

METHODS

We conducted a retrospective analysis of 83 patients with prostate cancer or prostatic hyperplasia who underwent DECT scans at Meizhou People's Hospital between July 2019 and December 2023. The variables analyzed included age, tumor diameter and serum prostate-specific antigen (PSA) levels, among others. We also compared CT values, signal-to-noise ratio (SNR), subjective image quality ratings, and contrast-to-noise ratio (CNR) between virtual monoenergetic images (40-100 keV) and conventional linear blending images. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic efficacy of virtual monoenergetic images (40 keV and 50 keV) compared to conventional images.

RESULTS

Virtual monoenergetic images at 40 keV showed significantly higher CT values (168.19 ± 57.14) compared to conventional linear blending images (66.66 ± 15.5) for prostate cancer (P < 0.001). The 50 keV images also demonstrated elevated CT values (121.73 ± 39.21) compared to conventional images (P < 0.001). CNR values for the 40 keV (3.81 ± 2.13) and 50 keV (2.95 ± 1.50) groups were significantly higher than the conventional blending group (P < 0.001). Subjective evaluations indicated markedly better image quality scores for 40 keV (median score of 5) and 50 keV (median score of 5) images compared to conventional images (P < 0.05). ROC curve analysis revealed superior diagnostic accuracy for 40 keV (AUC: 0.910) and 50 keV (AUC: 0.910) images based on CT values compared to conventional images (AUC: 0.849).

CONCLUSIONS

Virtual monoenergetic images reconstructed at 40 keV and 50 keV from DECT arterial phase scans substantially enhance the image quality of prostate lesions and improve diagnostic efficacy for prostate cancer.

An effective no-reference image quality index prediction with a hybrid Artificial Intelligence approach for denoised MRI images

As the quantity and significance of digital pictures in the medical industry continue to increase, Image Quality Assessment (IQA) has recently become a prevalent subject in the research community. Due to the wide range of distortions that Magnetic Resonance Images (MRI) can experience and the wide variety of information they contain, No-Reference Image Quality Assessment (NR-IQA) has always been a challenging study issue. In an attempt to address this issue, a novel hybrid Artificial Intelligence (AI) is proposed to analyze NR-IQ in massive MRI data. First, the features from the denoised MRI images are extracted using the gray level run length matrix (GLRLM) and EfficientNet B7 algorithm. Next, the Multi-Objective Reptile Search Algorithm (MRSA) was proposed for optimal feature vector selection. Then, the Self-evolving Deep Belief Fuzzy Neural network (SDBFN) algorithm was proposed for the effective NR-IQ analysis. The implementation of this research is executed using MATLAB software. The simulation results are compared with the various conventional methods in terms of correlation coefficient (PLCC), Root Mean Square Error (RMSE), Spearman Rank Order Correlation Coefficient (SROCC) and Kendall Rank Order Correlation Coefficient (KROCC), and Mean Absolute Error (MAE). In addition, our proposed approach yielded a quality number approximately we achieved significant 20% improvement than existing methods, with the PLCC parameter showing a notable increase compared to current techniques. Moreover, the RMSE number decreased by 12% when compared to existing methods. Graphical representations indicated mean MAE values of 0.02 for MRI knee dataset, 0.09 for MRI brain dataset, and 0.098 for MRI breast dataset, showcasing significantly lower MAE values compared to the baseline models.

Retinex theory-based nonlinear luminance enhancement and denoising for low-light endoscopic images

BACKGROUND

The quality of low-light endoscopic images involves applications in medical disciplines such as physiology and anatomy for the identification and judgement of tissue structures. Due to the use of point light sources and the constraints of narrow physiological structures, medical endoscopic images display uneven brightness, low contrast, and a lack of texture information, presenting diagnostic challenges for physicians.

METHODS

In this paper, a nonlinear brightness enhancement and denoising network based on Retinex theory is designed to improve the brightness and details of low-light endoscopic images. The nonlinear luminance enhancement module uses higher-order curvilinear functions to improve overall brightness; the dual-attention denoising module captures detailed features of anatomical structures; and the color loss function mitigates color distortion.

RESULTS

Experimental results on the Endo4IE dataset demonstrate that the proposed method outperforms existing state-of-the-art methods in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). The PSNR is 27.2202, SSIM is 0.8342, and the LPIPS is 0.1492. It provides a method to enhance image quality in clinical diagnosis and treatment.

CONCLUSIONS

It offers an efficient method to enhance images captured by endoscopes and offers valuable insights into intricate human physiological structures, which can effectively assist clinical diagnosis and treatment.

Metal implant segmentation in CT images based on diffusion model

BACKGROUND

Computed tomography (CT) is widely in clinics and is affected by metal implants. Metal segmentation is crucial for metal artifact correction, and the common threshold method often fails to accurately segment metals.

PURPOSE

This study aims to segment metal implants in CT images using a diffusion model and further validate it with clinical artifact images and phantom images of known size.

METHODS

A retrospective study was conducted on 100 patients who received radiation therapy without metal artifacts, and simulated artifact data were generated using publicly available mask data. The study utilized 11,280 slices for training and verification, and 2,820 slices for testing. Metal mask segmentation was performed using DiffSeg, a diffusion model incorporating conditional dynamic coding and a global frequency parser (GFParser). Conditional dynamic coding fuses the current segmentation mask and prior images at multiple scales, while GFParser helps eliminate high-frequency noise in the mask. Clinical artifact images and phantom images are also used for model validation.

RESULTS

Compared with the ground truth, the accuracy of DiffSeg for metal segmentation of simulated data was 97.89% and that of DSC was 95.45%. The mask shape obtained by threshold segmentation covered the ground truth and DSCs were 82.92% and 84.19% for threshold segmentation based on 2500 HU and 3000 HU. Evaluation metrics and visualization results show that DiffSeg performs better than other classical deep learning networks, especially for clinical CT, artifact data, and phantom data.

CONCLUSION

DiffSeg efficiently and robustly segments metal masks in artifact data with conditional dynamic coding and GFParser. Future work will involve embedding the metal segmentation model in metal artifact reduction to improve the reduction effect.

STC-UNet: renal tumor segmentation based on enhanced feature extraction at different network levels

Renal tumors are one of the common diseases of urology, and precise segmentation of these tumors plays a crucial role in aiding physicians to improve diagnostic accuracy and treatment effectiveness. Nevertheless, inherent challenges associated with renal tumors, such as indistinct boundaries, morphological variations, and uncertainties in size and location, segmenting renal tumors accurately remains a significant challenge in the field of medical image segmentation. With the development of deep learning, substantial achievements have been made in the domain of medical image segmentation. However, existing models lack specificity in extracting features of renal tumors across different network hierarchies, which results in insufficient extraction of renal tumor features and subsequently affects the accuracy of renal tumor segmentation. To address this issue, we propose the Selective Kernel, Vision Transformer, and Coordinate Attention Enhanced U-Net (STC-UNet). This model aims to enhance feature extraction, adapting to the distinctive characteristics of renal tumors across various network levels. Specifically, the Selective Kernel modules are introduced in the shallow layers of the U-Net, where detailed features are more abundant. By selectively employing convolutional kernels of different scales, the model enhances its capability to extract detailed features of renal tumors across multiple scales. Subsequently, in the deeper layers of the network, where feature maps are smaller yet contain rich semantic information, the Vision Transformer modules are integrated in a non-patch manner. These assist the model in capturing long-range contextual information globally. Their non-patch implementation facilitates the capture of fine-grained features, thereby achieving collaborative enhancement of global-local information and ultimately strengthening the model's extraction of semantic features of renal tumors. Finally, in the decoder segment, the Coordinate Attention modules embedding positional information are proposed aiming to enhance the model's feature recovery and tumor region localization capabilities. Our model is validated on the KiTS19 dataset, and experimental results indicate that compared to the baseline model, STC-UNet shows improvements of 1.60%, 2.02%, 2.27%, 1.18%, 1.52%, and 1.35% in IoU, Dice, Accuracy, Precision, Recall, and F1-score, respectively. Furthermore, the experimental results demonstrate that the proposed STC-UNet method surpasses other advanced algorithms in both visual effectiveness and objective evaluation metrics.

Enhancing ECG-based heart age: impact of acquisition parameters and generalization strategies for varying signal morphologies and corruptions

Electrocardiogram (ECG) is a non-invasive approach to capture the overall electrical activity produced by the contraction and relaxation of the cardiac muscles. It has been established in the literature that the difference between ECG-derived age and chronological age represents a general measure of cardiovascular health. Elevated ECG-derived age strongly correlates with cardiovascular conditions (e.g., atherosclerotic cardiovascular disease). However, the neural networks for ECG age estimation are yet to be thoroughly evaluated from the perspective of ECG acquisition parameters. Additionally, deep learning systems for ECG analysis encounter challenges in generalizing across diverse ECG morphologies in various ethnic groups and are susceptible to errors with signals that exhibit random or systematic distortions To address these challenges, we perform a comprehensive empirical study to determine the threshold for the sampling rate and duration of ECG signals while considering their impact on the computational cost of the neural networks. To tackle the concern of ECG waveform variability in different populations, we evaluate the feasibility of utilizing pre-trained and fine-tuned networks to estimate ECG age in different ethnic groups. Additionally, we empirically demonstrate that finetuning is an environmentally sustainable way to train neural networks, and it significantly decreases the ECG instances required (by more than 100 × ) for attaining performance similar to the networks trained from random weight initialization on a complete dataset. Finally, we systematically evaluate augmentation schemes for ECG signals in the context of age estimation and introduce a random cropping scheme that provides best-in-class performance while using shorter-duration ECG signals. The results also show that random cropping enables the networks to perform well with systematic and random ECG signal corruptions.

A deep learning-powered diagnostic model for acute pancreatitis

BACKGROUND

Acute pancreatitis is one of the most common diseases requiring emergency surgery. Rapid and accurate recognition of acute pancreatitis can help improve clinical outcomes. This study aimed to develop a deep learning-powered diagnostic model for acute pancreatitis.

MATERIALS AND METHODS

In this investigation, we enrolled a cohort of 190 patients with acute pancreatitis who were admitted to Sichuan Provincial People's Hospital between January 2020 and December 2021. Abdominal computed tomography (CT) scans were obtained from both patients with acute pancreatitis and healthy individuals. Our model was constructed using two modules: (1) the acute pancreatitis classifier module; (2) the pancreatitis lesion segmentation module. Each model's performance was assessed based on precision, recall rate, F1-score, Area Under the Curve (AUC), loss rate, frequency-weighted accuracy (fwavacc), and Mean Intersection over Union (MIOU).

RESULTS

Upon admission, significant variations were observed between patients with mild and severe acute pancreatitis in inflammatory indexes, liver, and kidney function indicators, as well as coagulation parameters. The acute pancreatitis classifier module exhibited commendable diagnostic efficacy, showing an impressive AUC of 0.993 (95%CI: 0.978-0.999) in the test set (comprising healthy examination patients vs. those with acute pancreatitis, P < 0.001) and an AUC of 0.850 (95%CI: 0.790-0.898) in the external validation set (healthy examination patients vs. patients with acute pancreatitis, P < 0.001). Furthermore, the acute pancreatitis lesion segmentation module demonstrated exceptional performance in the validation set. For pancreas segmentation, peripancreatic inflammatory exudation, peripancreatic effusion, and peripancreatic abscess necrosis, the MIOU values were 86.02 (84.52, 87.20), 61.81 (56.25, 64.83), 57.73 (49.90, 68.23), and 66.36 (55.08, 72.12), respectively. These findings underscore the robustness and reliability of the developed models in accurately characterizing and assessing acute pancreatitis.

CONCLUSION

The diagnostic model for acute pancreatitis, driven by deep learning, exhibits excellent efficacy in accurately evaluating the severity of the condition.

TRIAL REGISTRATION

This is a retrospective study.

Deep learning generation of preclinical positron emission tomography (PET) images from low-count PET with task-based performance assessment

BACKGROUND

Preclinical low-count positron emission tomography (LC-PET) imaging offers numerous advantages such as facilitating imaging logistics, enabling longitudinal studies of long- and short-lived isotopes as well as increasing scanner throughput. However, LC-PET is characterized by reduced photon-count levels resulting in low signal-to-noise ratio (SNR), segmentation difficulties, and quantification uncertainties.

PURPOSE

We developed and evaluated a novel deep-learning (DL) architecture-Attention based Residual-Dilated Net (ARD-Net)-to generate standard-count PET (SC-PET) images from LC-PET images. The performance of the ARD-Net framework was evaluated for numerous low count realizations using fidelity-based qualitative metrics, task-based segmentation, and quantitative metrics.

METHOD

Patient Derived tumor Xenograft (PDX) with tumors implanted in the mammary fat-pad were subjected to preclinical [18F]-Fluorodeoxyglucose (FDG)-PET/CT imaging. SC-PET images were derived from a 10 min static FDG-PET acquisition, 50 min post administration of FDG, and were resampled to generate four distinct LC-PET realizations corresponding to 10%, 5%, 1.6%, and 0.8% of SC-PET count-level. ARD-Net was trained and optimized using 48 preclinical FDG-PET datasets, while 16 datasets were utilized to assess performance. Further, the performance of ARD-Net was benchmarked against two leading DL-based methods (Residual UNet, RU-Net; and Dilated Network, D-Net) and non-DL methods (Non-Local Means, NLM; and Block Matching 3D Filtering, BM3D). The performance of the framework was evaluated using traditional fidelity-based image quality metrics such as Structural Similarity Index Metric (SSIM) and Normalized Root Mean Square Error (NRMSE), as well as human observer-based tumor segmentation performance (Dice Score and volume bias) and quantitative analysis of Standardized Uptake Value (SUV) measurements. Additionally, radiomics-derived features were utilized as a measure of quality assurance (QA) in comparison to true SC-PET. Finally, a performance ensemble score (EPS) was developed by integrating fidelity-based and task-based metrics. Concordance Correlation Coefficient (CCC) was utilized to determine concordance between measures. The non-parametric Friedman Test with Bonferroni correction was used to compare the performance of ARD-Net against benchmarked methods with significance at adjusted p-value ≤0.01.

RESULTS

ARD-Net-generated SC-PET images exhibited significantly better (p ≤ 0.01 post Bonferroni correction) overall image fidelity scores in terms of SSIM and NRMSE at majority of photon-count levels compared to benchmarked DL and non-DL methods. In terms of task-based quantitative accuracy evaluated by SUVMean and SUVPeak, ARD-Net exhibited less than 5% median absolute bias for SUVMean compared to true SC-PET and lower degree of variability compared to benchmarked DL and non-DL based methods in generating SC-PET. Additionally, ARD-Net-generated SC-PET images displayed higher degree of concordance to SC-PET images in terms of radiomics features compared to non-DL and other DL approaches. Finally, the ensemble score suggested that ARD-Net exhibited significantly superior performance compared to benchmarked algorithms (p ≤ 0.01 post Bonferroni correction).

CONCLUSION

ARD-Net provides a robust framework to generate SC-PET from LC-PET images. ARD-Net generated SC-PET images exhibited superior performance compared other DL and non-DL approaches in terms of image-fidelity based metrics, task-based segmentation metrics, and minimal bias in terms of task-based quantification performance for preclinical PET imaging.

Cross-Parallel Transformer: Parallel ViT for Medical Image Segmentation

Medical image segmentation primarily utilizes a hybrid model consisting of a Convolutional Neural Network and sequential Transformers. The latter leverage multi-head self-attention mechanisms to achieve comprehensive global context modelling. However, despite their success in semantic segmentation, the feature extraction process is inefficient and demands more computational resources, which hinders the network's robustness. To address this issue, this study presents two innovative methods: PTransUNet (PT model) and C-PTransUNet (C-PT model). The C-PT module refines the Vision Transformer by substituting a sequential design with a parallel one. This boosts the feature extraction capabilities of Multi-Head Self-Attention via self-correlated feature attention and channel feature interaction, while also streamlining the Feed-Forward Network to lower computational demands. On the Synapse public dataset, the PT and C-PT models demonstrate improvements in DSC accuracy by 0.87% and 3.25%, respectively, in comparison with the baseline model. As for the parameter count and FLOPs, the PT model aligns with the baseline model. In contrast, the C-PT model shows a decrease in parameter count by 29% and FLOPs by 21.4% relative to the baseline model. The proposed segmentation models in this study exhibit benefits in both accuracy and efficiency.

Recent advances in computerized imaging and its vital roles in liver disease diagnosis, preoperative planning, and interventional liver surgery: A review

The earliest and most accurate detection of the pathological manifestations of hepatic diseases ensures effective treatments and thus positive prognostic outcomes. In clinical settings, screening and determining the extent of a pathology are prominent factors in preparing remedial agents and administering appropriate therapeutic procedures. Moreover, in a patient undergoing liver resection, a realistic preoperative simulation of the subject-specific anatomy and physiology also plays a vital part in conducting initial assessments, making surgical decisions during the procedure, and anticipating postoperative results. Conventionally, various medical imaging modalities, e.g., computed tomography, magnetic resonance imaging, and positron emission tomography, have been employed to assist in these tasks. In fact, several standardized procedures, such as lesion detection and liver segmentation, are also incorporated into prominent commercial software packages. Thus far, most integrated software as a medical device typically involves tedious interactions from the physician, such as manual delineation and empirical adjustments, as per a given patient. With the rapid progress in digital health approaches, especially medical image analysis, a wide range of computer algorithms have been proposed to facilitate those procedures. They include pattern recognition of a liver, its periphery, and lesion, as well as pre- and postoperative simulations. Prior to clinical adoption, however, software must conform to regulatory requirements set by the governing agency, for instance, valid clinical association and analytical and clinical validation. Therefore, this paper provides a detailed account and discussion of the state-of-the-art methods for liver image analyses, visualization, and simulation in the literature. Emphasis is placed upon their concepts, algorithmic classifications, merits, limitations, clinical considerations, and future research trends.

Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017-2023

Cardiovascular diseases are a leading cause of mortality globally. Electrocardiography (ECG) still represents the benchmark approach for identifying cardiac irregularities. Automatic detection of abnormalities from the ECG can aid in the early detection, diagnosis, and prevention of cardiovascular diseases. Deep Learning (DL) architectures have been successfully employed for arrhythmia detection and classification and offered superior performance to traditional shallow Machine Learning (ML) approaches. This survey categorizes and compares the DL architectures used in ECG arrhythmia detection from 2017-2023 that have exhibited superior performance. Different DL models such as Convolutional Neural Networks (CNNs), Multilayer Perceptrons (MLPs), Transformers, and Recurrent Neural Networks (RNNs) are reviewed, and a summary of their effectiveness is provided. This survey provides a comprehensive roadmap to expedite the acclimation process for emerging researchers willing to develop efficient algorithms for detecting ECG anomalies using DL models. Our tailored guidelines bridge the knowledge gap allowing newcomers to align smoothly with the prevailing research trends in ECG arrhythmia detection. We shed light on potential areas for future research and refinement in model development and optimization, intending to stimulate advancement in ECG arrhythmia detection and classification.

Efficacy of fusion imaging for immediate post-ablation assessment of malignant liver neoplasms: A systematic review

BACKGROUND

Percutaneous thermal ablation has become the preferred therapeutic treatment option for liver cancers that cannot be resected. Since ablative zone tissue changes over time, it becomes challenging to determine therapy effectiveness over an extended period. Thus, an immediate post-procedural evaluation of the ablation zone is crucial, as it could influence the need for a second-look treatment or follow-up plan. Assessing treatment response immediately after ablation is essential to attain favorable outcomes. This study examines the efficacy of image fusion strategies immediately post-ablation in liver neoplasms to determine therapeutic response.

METHODOLOGY

A comprehensive systematic search using PRISMA methodology was conducted using EMBASE, MEDLINE (via PUBMED), and Cochrane Library Central Registry electronic databases to identify articles that assessed the immediate post-ablation response in malignant hepatic tumors with fusion imaging (FI) systems. The data were retrieved on relevant clinical characteristics, including population demographics, pre-intervention clinical history, lesion characteristics, and intervention type. For the outcome metrics, variables such as average fusion time, intervention metrics, technical success rate, ablative safety margin, supplementary ablation rate, technical efficacy rate, LTP rates, and reported complications were extracted.

RESULTS

Twenty-two studies were included for review after fulfilling the study eligibility criteria. FI's immediate technical success rate ranged from 81.3% to 100% in 17/22 studies. In 16/22 studies, the ablative safety margin was assessed immediately after ablation. Supplementary ablation was performed in 9 studies following immediate evaluation by FI. In 15/22 studies, the technical effectiveness rates during the first follow-up varied from 89.3% to 100%.

CONCLUSION

Based on the studies included, we found that FI can accurately determine the immediate therapeutic response in liver cancer ablation image fusion and could be a feasible intraprocedural tool for determining short-term post-ablation outcomes in unresectable liver neoplasms. There are some technical challenges that limit the widespread adoption of FI techniques. Large-scale randomized trials are warranted to improve on existing protocols. Future research should emphasize improving FI's technological capabilities and clinical applicability to a broader range of tumor types and ablation procedures.