Data Brief. 2024 Sep 11;57:110933. doi: 10.1016/j.dib.2024.110933. eCollection 2024 Dec.

ABSTRACT

This article presents an image dataset of palm leaf diseases to aid the early identification and classification of date palm infections. The dataset contains images of 8 main types of disorders affecting date palm leaves, three of which are physiological, four are fungal, and one is caused by pests. Specifically, the collected samples exhibit symptoms and signs of potassium deficiency, manganese deficiency, magnesium deficiency, black scorch, leaf spots, fusarium wilt, rachis blight, and parlatoria blanchardi. Moreover, the dataset includes a baseline of healthy palm leaves. In total, 608 raw images were captured over a period of three months, coinciding with the autumn and spring seasons, from 10 real date farms in the Madinah region of Saudi Arabia. The images were captured using smartphones and an SLR camera, focusing mainly on inflected leaves and leaflets. Date palm fruits, trunks, and roots are beyond the focus of this dataset. The infected leaf images were filtered, cropped, augmented, and categorized into their disease classes. The resulting processed dataset comprises 3089 images. Our proposed dataset can be used to train classification deep learning models of infected date palm leaves, thus enabling the early prevention of palm tree-related diseases.

PMID:39376482 | PMC:PMC11456780 | DOI:10.1016/j.dib.2024.110933

Data Brief. 2024 Sep 13;57:110928. doi: 10.1016/j.dib.2024.110928. eCollection 2024 Dec.

ABSTRACT

Urochloa grasses are widely used forages in the Neotropics and are gaining importance in other regions due to their role in meeting the increasing global demand for sustainable agricultural practices. High-throughput phenotyping (HTP) is important for accelerating Urochloa breeding programs focused on improving forage and seed yield. While RGB imaging has been used for HTP of vegetative traits, the assessment of phenological stages and seed yield using image analysis remains unexplored in this genus. This work presents a dataset of 2,400 high-resolution RGB images of 200 Urochloa hybrid genotypes, captured over seven months and covering both vegetative and reproductive stages. Images were manually labelled as vegetative or reproductive, and a subset of 255 reproductive stage images were annotated to identify 22,340 individual racemes. This dataset enables the development of machine learning and deep learning models for automated phenological stage classification and raceme identification, facilitating HTP and accelerated breeding of Urochloa spp. hybrids with high seed yield potential.

PMID:39376481 | PMC:PMC11456779 | DOI:10.1016/j.dib.2024.110928

Indian J Otolaryngol Head Neck Surg. 2024 Oct;76(5):4986-4996. doi: 10.1007/s12070-024-04885-4. Epub 2024 Jul 15.

ABSTRACT

This systematic literature review aims to study the role and impact of artificial intelligence (AI) in transforming Ear, Nose, and Throat (ENT) healthcare. It aims to compare and analyse literature that applied AI algorithms for ENT disease prediction and detection based on their effectiveness, methods, dataset, and performance. We have also discussed ENT specialists' challenges and AI's role in solving them. This review also discusses the challenges faced by AI researchers. This systematic review was completed using PRISMA guidelines. Data was extracted from several reputable digital databases, including PubMed, Medline, SpringerLink, Elsevier, Google Scholar, ScienceDirect, and IEEExplore. The search criteria included studies recently published between 2018 and 2024 related to the application of AI for ENT healthcare. After removing duplicate studies and quality assessments, we reviewed eligible articles and responded to the research questions. This review aims to provide a comprehensive overview of the current state of AI applications in ENT healthcare. Among the 3257 unique studies, 27 were selected as primary studies. About 62.5% of the included studies were effective in providing disease predictions. We found that Pretrained DL models are more in application than CNN algorithms when employed for ENT disease predictions. The accuracy of models ranged between 75 and 97%. We also observed the effectiveness of conversational AI models such as ChatGPT in the ENT discipline. The research in AI for ENT is advancing rapidly. Most of the models have achieved accuracy above 90%. However, the lack of good-quality data and data variability limits the overall ability of AI models to perform better for ENT disease prediction. Further research needs to be conducted while considering factors such as external validation and the issue of class imbalance.

PMID:39376323 | PMC:PMC11456104 | DOI:10.1007/s12070-024-04885-4

Indian J Otolaryngol Head Neck Surg. 2024 Oct;76(5):4036-4042. doi: 10.1007/s12070-024-04776-8. Epub 2024 Jun 6.

ABSTRACT

Background: Laryngeal cancer accounts for a third of all head and neck malignancies, necessitating timely detection for effective treatment and enhanced patient outcomes. Machine learning shows promise in medical diagnostics, but the impact of model complexity on diagnostic efficacy in laryngeal cancer detection can be ambiguous. Methods: In this study, we examine the relationship between model sophistication and diagnostic efficacy by evaluating three approaches: Logistic Regression, a small neural network with 4 layers of neurons and a more complex convolutional neural network with 50 layers and examine their efficacy on laryngeal cancer detection on computed tomography images. Results: Logistic regression achieved 82.5% accuracy. The 4-Layer NN reached 87.2% accuracy, while ResNet-50, a deep learning architecture, achieved the highest accuracy at 92.6%. Its deep learning capabilities excelled in discerning fine-grained CT image features. Conclusion: Our study highlights the choices involved in selecting a laryngeal cancer detection model. Logistic regression is interpretable but may struggle with complex patterns. The 4-Layer NN balances complexity and accuracy. ResNet-50 excels in image classification but demands resources. This research advances understanding affect machine learning model complexity could have on learning features of laryngeal tumor features in contrast CT images for purposes of disease prediction.

PMID:39376269 | PMC:PMC11455748 | DOI:10.1007/s12070-024-04776-8

Cancer Imaging. 2024 Oct 7;24(1):133. doi: 10.1186/s40644-024-00769-6.

ABSTRACT

Gliomas and Glioblastomas represent a significant portion of central nervous system (CNS) tumors associated with high mortality rates and variable prognosis. In 2021, the World Health Organization (WHO) updated its Glioma classification criteria, most notably incorporating molecular markers including CDKN2A/B homozygous deletion, TERT promoter mutation, EGFR amplification, + 7/-10 chromosome copy number changes, and others into the grading and classification of adult and pediatric Gliomas. The inclusion of these markers and the corresponding introduction of new Glioma subtypes has allowed for more specific tailoring of clinical interventions and has inspired a new wave of Radiogenomic studies seeking to leverage medical imaging information to explore the diagnostic and prognostic implications of these new biomarkers. Radiomics, deep learning, and combined approaches have enabled the development of powerful computational tools for MRI analysis correlating imaging characteristics with various molecular biomarkers integrated into the updated WHO CNS-5 guidelines. Recent studies have leveraged these methods to accurately classify Gliomas in accordance with these updated molecular-based criteria based solely on non-invasive MRI, demonstrating the great promise of Radiogenomic tools. In this review, we explore the relative benefits and drawbacks of these computational frameworks and highlight the technical and clinical innovations presented by recent studies in the landscape of fast evolving molecular-based Glioma subtyping. Furthermore, the potential benefits and challenges of incorporating these tools into routine radiological workflows, aiming to enhance patient care and optimize clinical outcomes in the evolving field of CNS tumor management, have been highlighted.

PMID:39375809 | DOI:10.1186/s40644-024-00769-6

J Cheminform. 2024 Oct 7;16(1):113. doi: 10.1186/s13321-024-00905-1.

ABSTRACT

In untargeted metabolomics, structures of small molecules are annotated using liquid chromatography-mass spectrometry by leveraging information from the molecular retention time (RT) in the chromatogram and m/z (formerly called ''mass-to-charge ratio'') in the mass spectrum. However, correct identification of metabolites is challenging due to the vast array of small molecules. Therefore, various in silico tools for mass spectrometry peak alignment and compound prediction have been developed; however, the list of candidate compounds remains extensive. Accurate RT prediction is important to exclude false candidates and facilitate metabolite annotation. Recent advancements in artificial intelligence (AI) have led to significant breakthroughs in the use of deep learning models in various fields. Release of a large RT dataset has mitigated the bottlenecks limiting the application of deep learning models, thereby improving their application in RT prediction tasks. This review lists the databases that can be used to expand training datasets and concerns the issue about molecular representation inconsistencies in datasets. It also discusses the application of AI technology for RT prediction, particularly in the 5 years following the release of the METLIN small molecule RT dataset. This review provides a comprehensive overview of the AI applications used for RT prediction, highlighting the progress and remaining challenges. SCIENTIFIC CONTRIBUTION: This article focuses on the advancements in small molecule retention time prediction in computational metabolomics over the past five years, with a particular emphasis on the application of AI technologies in this field. It reviews the publicly available datasets for small molecule retention time, the molecular representation methods, the AI algorithms applied in recent studies. Furthermore, it discusses the effectiveness of these models in assisting with the annotation of small molecule structures and the challenges that must be addressed to achieve practical applications.

PMID:39375739 | DOI:10.1186/s13321-024-00905-1

BMC Med Inform Decis Mak. 2024 Oct 7;24(1):288. doi: 10.1186/s12911-024-02676-z.

ABSTRACT

BACKGROUND: Histopathology is a gold standard for cancer diagnosis. It involves extracting tissue specimens from suspicious areas to prepare a glass slide for a microscopic examination. However, histological tissue processing procedures result in the introduction of artifacts, which are ultimately transferred to the digitized version of glass slides, known as whole slide images (WSIs). Artifacts are diagnostically irrelevant areas and may result in wrong predictions from deep learning (DL) algorithms. Therefore, detecting and excluding artifacts in the computational pathology (CPATH) system is essential for reliable automated diagnosis.

METHODS: In this paper, we propose a mixture of experts (MoE) scheme for detecting five notable artifacts, including damaged tissue, blur, folded tissue, air bubbles, and histologically irrelevant blood from WSIs. First, we train independent binary DL models as experts to capture particular artifact morphology. Then, we ensemble their predictions using a fusion mechanism. We apply probabilistic thresholding over the final probability distribution to improve the sensitivity of the MoE. We developed four DL pipelines to evaluate computational and performance trade-offs. These include two MoEs and two multiclass models of state-of-the-art deep convolutional neural networks (DCNNs) and vision transformers (ViTs). These DL pipelines are quantitatively and qualitatively evaluated on external and out-of-distribution (OoD) data to assess generalizability and robustness for artifact detection application.

RESULTS: We extensively evaluated the proposed MoE and multiclass models. DCNNs-based MoE and ViTs-based MoE schemes outperformed simpler multiclass models and were tested on datasets from different hospitals and cancer types, where MoE using (MobileNet) DCNNs yielded the best results. The proposed MoE yields 86.15 % F1 and 97.93% sensitivity scores on unseen data, retaining less computational cost for inference than MoE using ViTs. This best performance of MoEs comes with relatively higher computational trade-offs than multiclass models. Furthermore, we apply post-processing to create an artifact segmentation mask, a potential artifact-free RoI map, a quality report, and an artifact-refined WSI for further computational analysis. During the qualitative evaluation, field experts assessed the predictive performance of MoEs over OoD WSIs. They rated artifact detection and artifact-free area preservation, where the highest agreement translated to a Cohen Kappa of 0.82, indicating substantial agreement for the overall diagnostic usability of the DCNN-based MoE scheme.

CONCLUSIONS: The proposed artifact detection pipeline will not only ensure reliable CPATH predictions but may also provide quality control. In this work, the best-performing pipeline for artifact detection is MoE with DCNNs. Our detailed experiments show that there is always a trade-off between performance and computational complexity, and no straightforward DL solution equally suits all types of data and applications. The code and HistoArtifacts dataset can be found online at Github and Zenodo , respectively.

PMID:39375719 | DOI:10.1186/s12911-024-02676-z

BMC Med Imaging. 2024 Oct 7;24(1):264. doi: 10.1186/s12880-024-01442-x.

ABSTRACT

BACKGROUND: Invasive pulmonary aspergillosis (IPA) is a serious fungal infection. However, current diagnostic methods have limitations. The purpose of this study was to use artificial intelligence to achieve a more accurate diagnosis of IPA.

METHODS: Totally 263 patients (148 cases of IPA, 115 cases of non-IPA) were retrospectively enrolled from a single institution and randomly divided into training and test sets at a ratio of 7:3. Clinic-radiological independent risk factors for IPA were screened using univariate analysis and multivariate logistic regression analysis, after which a clinic-radiological model was constructed. The optimal radiomics features were extracted and screened based on CT images to construct the radiomics label score (Rad-score) and radiomics model. The optimal DL features were extracted and screened using four pre-trained convolutional neural networks, respectively, followed by the construction of the DL label score (DL-score) and DL model. Then, the radiomics-DL model was constructed. Finally, the combined model was constructed based on clinic-radiological independent risk factors, the Rad-score, and the DL-score. LR was adopted as the classifier. Receiver operating characteristic (ROC) curves were drawn, and the areas under the curve (AUC) were calculated to evaluate the efficacy of each model in predicting IPA. Additionally, based on the best-performing model on the LR classifier, four other machine learning (ML) classifiers were constructed to evaluate the predictive value for IPA.

RESULTS: The AUC of the clinic-radiological model for predicting IPA in the training and test sets was 0.845 and 0.765, respectively. The AUC of the radiomics-DL and combined models in the training set was 0.871 and 0.932, while in the test set was 0.851 and 0.881, respectively. The combined model showed better predictive performance than all other models. DCA showed that taking 0.00-1.00 as the threshold, the clinical benefit of the combined model was higher than that of all other models. Then, the combined model was trained on four other machine learning classifiers, all of which achieved AUC values above 0.80 in the test set, showing good performance in predicting IPA.

CONCLUSION: Clinic, CT radiomics, and DL combined model could be used to predict IPA effectively.

PMID:39375609 | DOI:10.1186/s12880-024-01442-x

BMC Med Res Methodol. 2024 Oct 7;24(1):232. doi: 10.1186/s12874-024-02357-5.

ABSTRACT

BACKGROUND: Postoperative pain is a prevalent symptom experienced by patients undergoing surgical procedures. This study aims to develop deep learning algorithms for predicting acute postoperative pain using both essential patient details and real-time vital sign data during surgery.

METHODS: Through a retrospective observational approach, we utilized Graph Attention Networks (GAT) and graph Transformer Networks (GTN) deep learning algorithms to construct the DoseFormer model while incorporating an attention mechanism. This model employed patient information and intraoperative vital signs obtained during Video-assisted thoracoscopic surgery (VATS) surgery to anticipate postoperative pain. By categorizing the static and dynamic data, the DoseFormer model performed binary classification to predict the likelihood of postoperative acute pain.

RESULTS: A total of 1758 patients were initially included, with 1552 patients after data cleaning. These patients were then divided into training set (n = 931) and testing set (n = 621). In the testing set, the DoseFormer model exhibited significantly higher AUROC (0.98) compared to classical machine learning algorithms. Furthermore, the DoseFormer model displayed a significantly higher F1 value (0.85) in comparison to other classical machine learning algorithms. Notably, the attending anesthesiologists' F1 values (attending: 0.49, fellow: 0.43, Resident: 0.16) were significantly lower than those of the DoseFormer model in predicting acute postoperative pain.

CONCLUSIONS: Deep learning model can predict postoperative acute pain events based on patients' basic information and intraoperative vital signs.

PMID:39375589 | DOI:10.1186/s12874-024-02357-5

NPJ Digit Med. 2024 Oct 7;7(1):275. doi: 10.1038/s41746-024-01271-w.

ABSTRACT

To address challenges in screening for chronic kidney disease (CKD), we devised a deep learning-based CKD screening model named UWF-CKDS. It utilizes ultra-wide-field (UWF) fundus images to predict the presence of CKD. We validated the model with data from 23 tertiary hospitals across China. Retinal vessels and retinal microvascular parameters (RMPs) were extracted to enhance model interpretability, which revealed a significant correlation between renal function and RMPs. UWF-CKDS, utilizing UWF images, RMPs, and relevant medical history, can accurately determine CKD status. Importantly, UWF-CKDS exhibited superior performance compared to CTR-CKDS, a model developed using the central region (CTR) cropped from UWF images, underscoring the contribution of the peripheral retina in predicting renal function. The study presents UWF-CKDS as a highly implementable method for large-scale and accurate CKD screening at the population level.

PMID:39375513 | DOI:10.1038/s41746-024-01271-w

Cell Res. 2024 Oct 8. doi: 10.1038/s41422-024-01034-y. Online ahead of print.

ABSTRACT

Deciphering universal gene regulatory mechanisms in diverse organisms holds great potential for advancing our knowledge of fundamental life processes and facilitating clinical applications. However, the traditional research paradigm primarily focuses on individual model organisms and does not integrate various cell types across species. Recent breakthroughs in single-cell sequencing and deep learning techniques present an unprecedented opportunity to address this challenge. In this study, we built an extensive dataset of over 120 million human and mouse single-cell transcriptomes. After data preprocessing, we obtained 101,768,420 single-cell transcriptomes and developed a knowledge-informed cross-species foundation model, named GeneCompass. During pre-training, GeneCompass effectively integrated four types of prior biological knowledge to enhance our understanding of gene regulatory mechanisms in a self-supervised manner. By fine-tuning for multiple downstream tasks, GeneCompass outperformed state-of-the-art models in diverse applications for a single species and unlocked new realms of cross-species biological investigations. We also employed GeneCompass to search for key factors associated with cell fate transition and showed that the predicted candidate genes could successfully induce the differentiation of human embryonic stem cells into the gonadal fate. Overall, GeneCompass demonstrates the advantages of using artificial intelligence technology to decipher universal gene regulatory mechanisms and shows tremendous potential for accelerating the discovery of critical cell fate regulators and candidate drug targets.

PMID:39375485 | DOI:10.1038/s41422-024-01034-y

Sci Rep. 2024 Oct 7;14(1):23342. doi: 10.1038/s41598-024-74230-w.

ABSTRACT

To evaluate the intra- and interdevice repeatability of microperimetry (MP) assessments in patients with diabetic macular edema (DME) two consecutive MP testings (45 fovea-centered stimuli, 4-2 staircase strategy) were performed using MP3 (NIDEK, Aichi, Japan) and MAIA (CenterVue, Padova, Italy), respectively. Intraretinal fluid (IRF) and ellipsoid zone (EZ) thickness were automatically segmented by published deep learning algorithms. Hard exudates (HEs) were annotated semi-automatically and disorganization of retinal inner layers (DRIL) was segmented manually. Point-to-point registration of MP stimuli to corresponding spectral-domain OCT (Spectralis, Heidelberg Engineering, Germany) locations was performed for both devices. Repeatability was assessed overall and in areas of disease-specific OCT biomarkers using Bland-Altmann coefficients of repeatability (CoR). A total of 3600 microperimetry stimuli were tested in 20 eyes with DME. Global CoR was high using both devices (MP3: ± 6.55 dB, MAIA: ± 7.69 dB). Higher retest variances were observed in stimuli with IRF (MP3: CoR ± 7.4 dB vs. ± 6.0 dB, p = 0.001, MAIA: CoR ± 9.2dB vs. ± 6.8 dB, p = 0.002) and DRIL on MP3 (CoR ± 6.9 dB vs. ± 3.2 dB, p < 0.001) compared to stimuli without. Repeatabilities were reduced in areas with thinner EZ layers (both p < 0.05). Fixation (Fuji classification) was relatively unstable independent of device and run. These findings emphasize taking higher caution using MP in patients with DME.

PMID:39375434 | DOI:10.1038/s41598-024-74230-w

Andrology. 2024 Oct 7. doi: 10.1111/andr.13773. Online ahead of print.

ABSTRACT

BACKGROUND: Histological analysis of the testicular sections is paramount in infertility research but tedious and often requires months of training and practice.

OBJECTIVES: Establish an expeditious histopathological analysis of mutant mice testicular sections stained with commonly available hematoxylin and eosin (H&E) via enhanced deep learning model MATERIALS AND METHODS: Automated segmentation and cellular composition analysis on the testes of six mouse reproductive mutants of key reproductive gene family, DAZ and PUMILIO gene family via H&E-stained mouse testicular sections.

RESULTS: We improved the deep learning model with human interaction to achieve better pixel accuracy and reduced annotation time for histologists; revealed distinctive cell composition features consistent with previously published phenotypes for four mutants and novel spermatogenic defects in two newly generated mutants; established a fast spermatogenic defect detection protocol for quantitative and qualitative assessment of testicular defects within 2.5-3 h, requiring as few as 8 H&E-stained testis sections; uncovered novel defects in AcDKO and a meiotic arrest defect in HDBKO, supporting the synergistic interaction of Sertoli Pum1 and Pum2 as well as redundant meiotic function of Dazl and Boule.

DISCUSSION: Our testicular compositional analysis not only could reveal spermatogenic defects from staged seminiferous tubules but also from unstaged seminiferous tubule sections.

CONCLUSION: Our SCSD-Net model offers a rapid protocol for detecting reproductive defects from H&E-stained testicular sections in as few as 3 h, providing both quantitative and qualitative assessments of spermatogenic defects. Our analysis uncovered evidence supporting the synergistic interaction of Sertoli PUM1 and PUM2 in maintaining average testis size, and redundant roles of DAZ family proteins DAZL and BOULE in meiosis.

PMID:39375288 | DOI:10.1111/andr.13773

J Imaging Inform Med. 2024 Oct 7. doi: 10.1007/s10278-024-01286-5. Online ahead of print.

ABSTRACT

Trustworthiness is crucial for artificial intelligence (AI) models in clinical settings, and a fundamental aspect of trustworthy AI is uncertainty quantification (UQ). Conformal prediction as a robust uncertainty quantification (UQ) framework has been receiving increasing attention as a valuable tool in improving model trustworthiness. An area of active research is the method of non-conformity score calculation for conformal prediction. We propose deep conformal supervision (DCS), which leverages the intermediate outputs of deep supervision for non-conformity score calculation, via weighted averaging based on the inverse of mean calibration error for each stage. We benchmarked our method on two publicly available datasets focused on medical image classification: a pneumonia chest radiography dataset and a preprocessed version of the 2019 RSNA Intracranial Hemorrhage dataset. Our method achieved mean coverage errors of 16e-4 (CI: 1e-4, 41e-4) and 5e-4 (CI: 1e-4, 10e-4) compared to baseline mean coverage errors of 28e-4 (CI: 2e-4, 64e-4) and 21e-4 (CI: 8e-4, 3e-4) on the two datasets, respectively (p < 0.001 on both datasets). Based on our findings, the baseline results of conformal prediction already exhibit small coverage errors. However, our method shows a significant improvement on coverage error, particularly noticeable in scenarios involving smaller datasets or when considering smaller acceptable error levels, which are crucial in developing UQ frameworks for healthcare AI applications.

PMID:39375270 | DOI:10.1007/s10278-024-01286-5

J Chem Theory Comput. 2024 Oct 7. doi: 10.1021/acs.jctc.4c00669. Online ahead of print.

ABSTRACT

The folding and unfolding of RNA stem-loops are critical biological processes; however, their computational studies are often hampered by the ruggedness of their folding landscape, necessitating long simulation times at the atomistic scale. Here, we adapted DeepDriveMD (DDMD), an advanced deep learning-driven sampling technique originally developed for protein folding, to address the challenges of RNA stem-loop folding. Although tempering- and order parameter-based techniques are commonly used for similar rare-event problems, the computational costs or the need for a priori knowledge about the system often present a challenge in their effective use. DDMD overcomes these challenges by adaptively learning from an ensemble of running MD simulations using generic contact maps as the raw input. DeepDriveMD enables on-the-fly learning of a low-dimensional latent representation and guides the simulation toward the undersampled regions while optimizing the resources to explore the relevant parts of the phase space. We showed that DDMD estimates the free energy landscape of the RNA stem-loop reasonably well at room temperature. Our simulation framework runs at a constant temperature without external biasing potential, hence preserving the information on transition rates, with a computational cost much lower than that of the simulations performed with external biasing potentials. We also introduced a reweighting strategy for obtaining unbiased free energy surfaces and presented a qualitative analysis of the latent space. This analysis showed that the latent space captures the relevant slow degrees of freedom for the RNA folding problem of interest. Finally, throughout the manuscript, we outlined how different parameters are selected and optimized to adapt DDMD for this system. We believe this compendium of decision-making processes will help new users adapt this technique for the rare-event sampling problems of their interest.

PMID:39374435 | DOI:10.1021/acs.jctc.4c00669

J Appl Clin Med Phys. 2024 Oct 7:e14540. doi: 10.1002/acm2.14540. Online ahead of print.

ABSTRACT

This review examines the recent developments in deep learning (DL) techniques applied to multimodal fusion image segmentation for liver cancer. Hepatocellular carcinoma is a highly dangerous malignant tumor that requires accurate image segmentation for effective treatment and disease monitoring. Multimodal image fusion has the potential to offer more comprehensive information and more precise segmentation, and DL techniques have achieved remarkable progress in this domain. This paper starts with an introduction to liver cancer, then explains the preprocessing and fusion methods for multimodal images, then explores the application of DL methods in this area. Various DL architectures such as convolutional neural networks (CNN) and U-Net are discussed and their benefits in multimodal image fusion segmentation. Furthermore, various evaluation metrics and datasets currently used to measure the performance of segmentation models are reviewed. While reviewing the progress, the challenges of current research, such as data imbalance, model generalization, and model interpretability, are emphasized and future research directions are suggested. The application of DL in multimodal image segmentation for liver cancer is transforming the field of medical imaging and is expected to further enhance the accuracy and efficiency of clinical decision making. This review provides useful insights and guidance for medical practitioners.

PMID:39374312 | DOI:10.1002/acm2.14540

J Appl Clin Med Phys. 2024 Oct 7:e14546. doi: 10.1002/acm2.14546. Online ahead of print.

ABSTRACT

PURPOSE: Partial stereotactic ablative boost radiotherapy (P-SABR) effectively treats bulky lung cancer; however, the planning process for P-SABR requires repeated dose calculations. To improve planning efficiency, we proposed a novel deep learning method that utilizes limited data to accurately predict the three-dimensional (3D) dose distribution of the P-SABR plan for bulky lung cancer.

METHODS: We utilized data on 74 patients diagnosed with bulky lung cancer who received P-SABR treatment. The patient dataset was randomly divided into a training set (51 plans) with augmentation, validation set (7 plans), and testing set (16 plans). We devised a 3D multi-scale dilated network (MD-Net) and integrated a scale-balanced structure loss into the loss function. A comparative analysis with a classical network and other advanced networks with multi-scale analysis capabilities and other loss functions was conducted based on the dose distributions in terms of the axial view, average dose scores (ADSs), and average absolute differences of dosimetric indices (AADDIs). Finally, we analyzed the predicted dosimetric indices against the ground-truth values and compared the predicted dose-volume histogram (DVH) with the ground-truth DVH.

RESULTS: Our proposed dose prediction method for P-SABR plans for bulky lung cancer demonstrated strong performance, exhibiting a significant improvement in predicting multiple indicators of regions of interest (ROIs), particularly the gross target volume (GTV). Our network demonstrated increased accuracy in most dosimetric indices and dose scores in different ROIs. The proposed loss function significantly enhanced the predictive performance of the dosimetric indices. The predicted dosimetric indices and DVHs were equivalent to the ground-truth values.

CONCLUSION: Our study presents an effective model based on limited datasets, and it exhibits high accuracy in the dose prediction of P-SABR plans for bulky lung cancer. This method has potential as an automated tool for P-SABR planning and can help optimize treatments and improve planning efficiency.

PMID:39374302 | DOI:10.1002/acm2.14546

IEEE Trans Med Imaging. 2024 Oct 7;PP. doi: 10.1109/TMI.2024.3475516. Online ahead of print.

ABSTRACT

Computed tomography (CT) stands as a ubiquitous medical diagnostic tool. Nonetheless, the radiation-related concerns associated with CT scans have raised public apprehensions. Mitigating radiation dosage in CT imaging poses an inherent challenge as it inevitably compromises the fidelity of CT reconstructions, impacting diagnostic accuracy. While previous deep learning techniques have exhibited promise in enhancing CT reconstruction quality, they remain hindered by the reliance on paired data, which is arduous to procure. In this study, we present a novel approach named Variational Score Solver (VSS) for solving sparse-view reconstruction without paired data. Our approach entails the acquisition of a probability distribution from densely sampled CT reconstructions, employing a latent diffusion model. High-quality reconstruction outcomes are achieved through an iterative process, wherein the diffusion model serves as the prior term, subsequently integrated with the data consistency term. Notably, rather than directly employing the prior diffusion model, we distill prior knowledge by finding the fixed point of the diffusion model. This framework empowers us to exercise precise control over the process. Moreover, we depart from modeling the reconstruction outcomes as deterministic values, opting instead for a distribution-based approach. This enables us to achieve more accurate reconstructions utilizing a trainable model. Our approach introduces a fresh perspective to the realm of zero-shot CT reconstruction, circumventing the constraints of supervised learning. Our extensive qualitative and quantitative experiments unequivocally demonstrate that VSS surpasses other contemporary unsupervised and achieves comparable results compared with the most advance supervised methods in sparse-view reconstruction tasks. Codes are available in https://github.com/fpsandnoob/vss.

PMID:39374276 | DOI:10.1109/TMI.2024.3475516

PLoS One. 2024 Oct 7;19(10):e0307432. doi: 10.1371/journal.pone.0307432. eCollection 2024.

ABSTRACT

Recently, there has been growing interest in deep spectral methods for image localization and segmentation, influenced by traditional spectral segmentation approaches. These methods reframe the image decomposition process as a graph partitioning task by extracting features using self-supervised learning and utilizing the Laplacian of the affinity matrix to obtain eigensegments. However, instance segmentation has received less attention than other tasks within the context of deep spectral methods. This paper addresses that not all channels of the feature map extracted from a self-supervised backbone contain sufficient information for instance segmentation purposes. Some channels are noisy and hinder the accuracy of the task. To overcome this issue, this paper proposes two channel reduction modules, Noise Channel Reduction (NCR) and Deviation-based Channel Reduction (DCR). The NCR retains channels with lower entropy, as they are less likely to be noisy, while DCR prunes channels with low standard deviation, as they lack sufficient information for effective instance segmentation. Furthermore, the paper demonstrates that the dot product, commonly used in deep spectral methods, is not suitable for instance segmentation due to its sensitivity to feature map values, potentially leading to incorrect instance segments. A novel similarity metric called Bray-curtis over Chebyshev (BoC) is proposed to address this issue. This metric considers the distribution of features in addition to their values, providing a more robust similarity measure for instance segmentation. Quantitative and qualitative results on the Youtube-VIS 2019 and OVIS datasets highlight the improvements achieved by the proposed channel reduction methods and using BoC instead of the conventional dot product for creating the affinity matrix. These improvements regarding mean Intersection over Union (mIoU) and extracted instance segments are observed, demonstrating enhanced instance segmentation performance. The code is available on: https://github.com/farnooshar/SpecUnIIS.

PMID:39374253 | DOI:10.1371/journal.pone.0307432

Med Phys. 2024 Oct 7. doi: 10.1002/mp.17440. Online ahead of print.

ABSTRACT

BACKGROUND: Multi-organ segmentation aids in disease diagnosis, treatment, and radiotherapy. The recently emerged photon counting detector-based CT (PCCT) provides spectral information of the organs and the background tissue and may improve segmentation performance.

PURPOSE: We propose UNet-based multi-organ segmentation in PCCT using virtual monoenergetic images (VMI) to exploit spectral information effectively.

METHODS: The proposed method consists of the following steps: Noise reduction in bin-wise images, image-based material decomposition, generating VMIs, and deep learning-based segmentation. VMIs are synthesized for various x-ray energies using basis images. The UNet-based networks (3D UNet, Swin UNETR) were used for segmentation, and dice similarity coefficients (DSC) and 3D visualization of the segmented result were evaluation indicators. We validated the proposed method for the liver, pancreas, and spleen segmentation using abdominal phantoms from 55 subjects for dual- and quad-energy bins. We compared it to the conventional PCCT-based segmentation, which uses only the (noise-reduced) bin-wise images. The experiments were conducted on two cases by adjusting the dose levels.

RESULTS: The proposed method improved the training stability for most cases. With the proposed method, the average DSC for the three organs slightly increased from 0.933 to 0.95, and the standard deviation decreased from 0.066 to 0.047, for example, in the low dose case (using VMIs v.s. bin-wise images from dual-energy bins; 3D UNet).

CONCLUSIONS: The proposed method using VMIs improves training stability for multi-organ segmentation in PCCT, particularly when the number of energy bins is small.

PMID:39374095 | DOI:10.1002/mp.17440