Special Issue "Image Processing and Analysis for Preclinical and Clinical Applications"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Alessandro Stefano
E-Mail Website1 Website2
Guest Editor
Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy
Interests: biomedical image analysis; metabolic imaging; preclinical studies; radiomics; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Albert Comelli
E-Mail Website
Guest Editor
1. Ri.MED Foundation, via Bandiera 11, 90133 Palermo, Italy
2. Research Affiliate Long Term—Laboratory of Computational Computer Vision (LCCV) in the School of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, GA, USA
3. Research Affiliate—Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Cefalù (PA), Italy
Interests: biomedical image processing and analysis; radiomics; artificial intelligence; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Federica Vernuccio
E-Mail Website
Guest Editor
Department of Biomedicine, Neuroscience and Advanced Diagnostics (BIND), University Hospital of Palermo, Via del Vespro 129, 90127 Palermo, Italy
Interests: liver imaging; pancreatic imaging; hepatocellular carcinoma; radiomics; texture analysis; diffuse liver diseases; emergency radiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Preclinical and clinical imaging aims to characterize and measure biological processes and diseases in animals and humans. In recent years, there has been growing interest in the quantitative analysis of clinical images using techniques such as Positron Emission Tomography, Computerized Tomography, and Magnetic Resonance Imaging, mainly applied to texture analysis and radiomics. In particular, various image processing and analysis algorithms based on pattern recognition, artificial intelligence, and computer graphics methods have been proposed to extract features from biomedical images. These quantitative approaches are expected to have a positive clinical impact on quantitatively analyzing images, reveal biological processes and diseases, and predict response to treatment.

This Special Issue, entitled “Image Processing and Analysis for Preclinical and Clinical Applications”, will present a collection of high-quality studies covering the state-of-the-art and innovative approaches focusing on image processing and analysis across a variety of imaging modalities as well as the expected clinical applicability of these innovative approaches for personalized patient-tailored medicine.

Dr. Alessandro Stefano
Dr. Albert Comelli
Dr. Federica Vernuccio
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • in-vivo imaging
  • therapy response prediction
  • medical diagnosis support systems
  • detection, segmentation, and classification of tissues
  • biomedical image analysis and processing
  • personalized medicine
  • artificial intelligence
  • texture analysis
  • radiomics

Published Papers (8 papers)

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Research

Article
Fundus Image Registration Technique Based on Local Feature of Retinal Vessels
Appl. Sci. 2021, 11(23), 11201; https://doi.org/10.3390/app112311201 - 25 Nov 2021
Viewed by 161
Abstract
Feature-based retinal fundus image registration (RIR) technique aligns fundus images according to geometrical transformations estimated between feature point correspondences. To ensure accurate registration, the feature points extracted must be from the retinal vessels and throughout the image. However, noises in the fundus image [...] Read more.
Feature-based retinal fundus image registration (RIR) technique aligns fundus images according to geometrical transformations estimated between feature point correspondences. To ensure accurate registration, the feature points extracted must be from the retinal vessels and throughout the image. However, noises in the fundus image may resemble retinal vessels in local patches. Therefore, this paper introduces a feature extraction method based on a local feature of retinal vessels (CURVE) that incorporates retinal vessels and noises characteristics to accurately extract feature points on retinal vessels and throughout the fundus image. The CURVE performance is tested on CHASE, DRIVE, HRF and STARE datasets and compared with six feature extraction methods used in the existing feature-based RIR techniques. From the experiment, the feature extraction accuracy of CURVE (86.021%) significantly outperformed the existing feature extraction methods (p ≤ 0.001*). Then, CURVE is paired with a scale-invariant feature transform (SIFT) descriptor to test its registration capability on the fundus image registration (FIRE) dataset. Overall, CURVE-SIFT successfully registered 44.030% of the image pairs while the existing feature-based RIR techniques (GDB-ICP, Harris-PIIFD, Ghassabi’s-SIFT, H-M 16, H-M 17 and D-Saddle-HOG) only registered less than 27.612% of the image pairs. The one-way ANOVA analysis showed that CURVE-SIFT significantly outperformed GDB-ICP (p = 0.007*), Harris-PIIFD, Ghassabi’s-SIFT, H-M 16, H-M 17 and D-Saddle-HOG (p ≤ 0.001*). Full article
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Article
Robustness of PET Radiomics Features: Impact of Co-Registration with MRI
Appl. Sci. 2021, 11(21), 10170; https://doi.org/10.3390/app112110170 - 30 Oct 2021
Viewed by 252
Abstract
Radiomics holds great promise in the field of cancer management. However, the clinical application of radiomics has been hampered by uncertainty about the robustness of the features extracted from the images. Previous studies have reported that radiomics features are sensitive to changes in [...] Read more.
Radiomics holds great promise in the field of cancer management. However, the clinical application of radiomics has been hampered by uncertainty about the robustness of the features extracted from the images. Previous studies have reported that radiomics features are sensitive to changes in voxel size resampling and interpolation, image perturbation, or slice thickness. This study aims to observe the variability of positron emission tomography (PET) radiomics features under the impact of co-registration with magnetic resonance imaging (MRI) using the difference percentage coefficient, and the Spearman’s correlation coefficient for three groups of images: (i) original PET, (ii) PET after co-registration with T1-weighted MRI and (iii) PET after co-registration with FLAIR MRI. Specifically, seventeen patients with brain cancers undergoing [11C]-Methionine PET were considered. Successively, PET images were co-registered with MRI sequences and 107 features were extracted for each mentioned group of images. The variability analysis revealed that shape features, first-order features and two subgroups of higher-order features possessed a good robustness, unlike the remaining groups of features, which showed large differences in the difference percentage coefficient. Furthermore, using the Spearman’s correlation coefficient, approximately 40% of the selected features differed from the three mentioned groups of images. This is an important consideration for users conducting radiomics studies with image co-registration constraints to avoid errors in cancer diagnosis, prognosis, and clinical outcome prediction. Full article
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Article
Pathologic Complete Response Prediction after Neoadjuvant Chemoradiation Therapy for Rectal Cancer Using Radiomics and Deep Embedding Network of MRI
Appl. Sci. 2021, 11(20), 9494; https://doi.org/10.3390/app11209494 - 13 Oct 2021
Viewed by 351
Abstract
Assessment of magnetic resonance imaging (MRI) after neoadjuvant chemoradiation therapy (nCRT) is essential in rectal cancer staging and treatment planning. However, when predicting the pathologic complete response (pCR) after nCRT for rectal cancer, existing works either rely on simple quantitative evaluation based on [...] Read more.
Assessment of magnetic resonance imaging (MRI) after neoadjuvant chemoradiation therapy (nCRT) is essential in rectal cancer staging and treatment planning. However, when predicting the pathologic complete response (pCR) after nCRT for rectal cancer, existing works either rely on simple quantitative evaluation based on radiomics features or partially analyze multi-parametric MRI. We propose an effective pCR prediction method based on novel multi-parametric MRI embedding. We first seek to extract volumetric features of tumors that can be found only by analyzing multiple MRI sequences jointly. Specifically, we encapsulate multiple MRI sequences into multi-sequence fusion images (MSFI) and generate MSFI embedding. We merge radiomics features, which capture important characteristics of tumors, with MSFI embedding to generate multi-parametric MRI embedding and then use it to predict pCR using a random forest classifier. Our extensive experiments demonstrate that using all given MRI sequences is the most effective regardless of the dimension reduction method. The proposed method outperformed any variants with different combinations of feature vectors and dimension reduction methods or different classification models. Comparative experiments demonstrate that it outperformed four competing baselines in terms of the AUC and F1-score. We use MRI sequences from 912 patients with rectal cancer, a much larger sample than in any existing work. Full article
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Article
ZFTool: A Software for Automatic Quantification of Cancer Cell Mass Evolution in Zebrafish
Appl. Sci. 2021, 11(16), 7721; https://doi.org/10.3390/app11167721 - 22 Aug 2021
Viewed by 555
Abstract
Background: Zebrafish (Danio rerio) is a model organism for the study of human cancer. Compared with the murine model, the zebrafish model has several properties ideal for personalized therapies. The transparency of the zebrafish embryos and the development of the pigment-deficient [...] Read more.
Background: Zebrafish (Danio rerio) is a model organism for the study of human cancer. Compared with the murine model, the zebrafish model has several properties ideal for personalized therapies. The transparency of the zebrafish embryos and the development of the pigment-deficient ”casper“ zebrafish line give the capacity to directly observe cancer formation and progression in the living animal. Automatic quantification of cellular proliferation in vivo is critical to the development of personalized medicine. Methods: A new methodology was defined to automatically quantify the cancer cellular evolution. ZFTool was developed to establish a base threshold that eliminates the embryo autofluorescence, automatically measures the area and intensity of GFP (green-fluorescent protein) marked cells, and defines a proliferation index. Results: The proliferation index automatically computed on different targets demonstrates the efficiency of ZFTool to provide a good automatic quantification of cancer cell evolution and dissemination. Conclusion: Our results demonstrate that ZFTool is a reliable tool for the automatic quantification of the proliferation index as a measure of cancer mass evolution in zebrafish, eliminating the influence of its autofluorescence. Full article
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Article
Transfer Learning for an Automated Detection System of Fractures in Patients with Maxillofacial Trauma
Appl. Sci. 2021, 11(14), 6293; https://doi.org/10.3390/app11146293 - 07 Jul 2021
Viewed by 620
Abstract
An original maxillofacial fracture detection system (MFDS), based on convolutional neural networks and transfer learning, is proposed to detect traumatic fractures in patients. A convolutional neural network pre-trained on non-medical images was re-trained and fine-tuned using computed tomography (CT) scans to produce a [...] Read more.
An original maxillofacial fracture detection system (MFDS), based on convolutional neural networks and transfer learning, is proposed to detect traumatic fractures in patients. A convolutional neural network pre-trained on non-medical images was re-trained and fine-tuned using computed tomography (CT) scans to produce a model for the classification of future CTs as either “fracture” or “noFracture”. The model was trained on a total of 148 CTs (120 patients labeled with “fracture” and 28 patients labeled with “noFracture”). The validation dataset, used for statistical analysis, was characterized by 30 patients (5 with “noFracture” and 25 with “fracture”). An additional 30 CT scans, comprising 25 “fracture” and 5 “noFracture” images, were used as the test dataset for final testing. Tests were carried out both by considering the single slices and by grouping the slices for patients. A patient was categorized as fractured if two consecutive slices were classified with a fracture probability higher than 0.99. The patients’ results show that the model accuracy in classifying the maxillofacial fractures is 80%. Even if the MFDS model cannot replace the radiologist’s work, it can provide valuable assistive support, reducing the risk of human error, preventing patient harm by minimizing diagnostic delays, and reducing the incongruous burden of hospitalization. Full article
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Article
Left Atrial Flow Stasis in Patients Undergoing Pulmonary Vein Isolation for Paroxysmal Atrial Fibrillation Using 4D-Flow Magnetic Resonance Imaging
Appl. Sci. 2021, 11(12), 5432; https://doi.org/10.3390/app11125432 - 11 Jun 2021
Viewed by 665
Abstract
Atrial fibrillation (AF) is associated with systemic thrombo-embolism and stroke events, which do not appear significantly reduced following successful pulmonary vein (PV) ablation. Prior studies supported that thrombus formation is associated with left atrial (LA) flow alterations, particularly flow stasis. Recently, time-resolved three-dimensional [...] Read more.
Atrial fibrillation (AF) is associated with systemic thrombo-embolism and stroke events, which do not appear significantly reduced following successful pulmonary vein (PV) ablation. Prior studies supported that thrombus formation is associated with left atrial (LA) flow alterations, particularly flow stasis. Recently, time-resolved three-dimensional phase-contrast (4D-flow) showed the ability to quantify LA stasis. This study aims to demonstrate that LA stasis, derived from 4D-flow, is a useful biomarker of LA recovery in patients with AF. Our hypothesis is that LA recovery will be associated with a reduction in LA stasis. We recruited 148 subjects with paroxysmal AF (40 following 3–4 months PV ablation and 108 pre-PV ablation) and 24 controls (CTL). All subjects underwent a cardiac magnetic resonance imaging (MRI) exam, inclusive of 4D-flow. LA was isolated within the 4D-flow dataset to constrain stasis maps. Control mean LA stasis was lower than in the pre-ablation cohort (30 ± 12% vs. 47 ± 18%, p < 0.001). In addition, mean LA stasis was reduced in the post-ablation cohort compared with pre-ablation (36 ± 15% vs. 47 ± 18%, p = 0.002). This study demonstrated that 4D flow-derived LA stasis mapping is clinically relevant and revealed stasis changes in the LA body pre- and post-pulmonary vein ablation. Full article
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Article
Early Monitoring Response to Therapy in Patients with Brain Lesions Using the Cumulative SUV Histogram
Appl. Sci. 2021, 11(7), 2999; https://doi.org/10.3390/app11072999 - 27 Mar 2021
Viewed by 473
Abstract
Gamma Knife treatment is an alternative to traditional brain surgery and whole-brain radiation therapy for treating cancers that are inaccessible via conventional treatments. To assess the effectiveness of Gamma Knife treatments, functional imaging can play a crucial role. The aim of this study [...] Read more.
Gamma Knife treatment is an alternative to traditional brain surgery and whole-brain radiation therapy for treating cancers that are inaccessible via conventional treatments. To assess the effectiveness of Gamma Knife treatments, functional imaging can play a crucial role. The aim of this study is to evaluate new prognostic indices to perform an early assessment of treatment response to therapy using positron emission tomography imaging. The parameters currently used in nuclear medicine assessments can be affected by statistical fluctuation errors and/or cannot provide information on tumor extension and heterogeneity. To overcome these limitations, the Cumulative standardized uptake value (SUV) Histogram (CSH) and Area Under the Curve (AUC) indices were evaluated to obtain additional information on treatment response. For this purpose, the absolute level of [11C]-Methionine (MET) uptake was measured and its heterogeneity distribution within lesions was evaluated by calculating the CSH and AUC indices. CSH and AUC parameters show good agreement with patient outcomes after Gamma Knife treatments. Furthermore, no relevant correlations were found between CSH and AUC indices and those usually used in the nuclear medicine environment. CSH and AUC indices could be a useful tool for assessing patient responses to therapy. Full article
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Article
Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging
Appl. Sci. 2021, 11(2), 782; https://doi.org/10.3390/app11020782 - 15 Jan 2021
Cited by 16 | Viewed by 1364
Abstract
Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, [...] Read more.
Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardware availability while still achieving accurate segmentation. We apply these models to a limited set of 85 manual prostate segmentations using the k-fold validation strategy and the Tversky loss function and we compare their results. We find that ENet and UNet are more accurate than ERFNet, with ENet much faster than UNet. Specifically, ENet obtains a dice similarity coefficient of 90.89% and a segmentation time of about 6 s using central processing unit (CPU) hardware to simulate real clinical conditions where graphics processing unit (GPU) is not always available. In conclusion, ENet could be efficiently applied for prostate delineation even in small image training datasets with potential benefit for patient management personalization. Full article
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