Special Issue "Advanced Computational Methods for Oncological Image Analysis"

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Medical Imaging".

Deadline for manuscript submissions: 31 July 2021.

Special Issue Editors

Dr. Leonardo Rundo
E-Mail Website
Guest Editor
Department of Radiology, University of Cambridge, Cambridge CB2 1TN, UK
Interests: medical image analysis; digital image processing; machine learning; computational intelligence; soft computing; oncological imaging; radiomics
Special Issues and Collections in MDPI journals
Dr. Carmelo Militello
E-Mail Website
Guest Editor
Institute of Molecular Bioimaging and Physiology, Italian National Research Council (IBFM-CNR), Cefalù (PA), Italy
Interests: digital image analysis and processing; biomedical imaging; radiomics; applied machine learning; digital architectures; hardware programmable devices
Special Issues and Collections in MDPI journals
Dr. Vincenzo Conti
E-Mail Website
Guest Editor
Faculty of Engineering and Architecture, University of Enna KORE, Enna, Italy
Interests: biometric recognition systems; programmable architectures; bio-inspired processing systems
Dr. Fulvio Zaccagna
E-Mail Website
Guest Editor
Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
Interests: biomedical image analysis; radiomics; machine learning; neuroimaging; neuro-oncology; metabolic imaging
Dr. Changhee Han
E-Mail Website
Guest Editor
LPIXEL Inc., Tokyo, Japan
Interests: machine learning; deep learning; medical imaging; bioinformatics

Special Issue Information

Dear Colleagues,

Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current challenge clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to the specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Towards this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies. This methodology provides reliable diagnostic and prognostic biomarkers for precision oncology.

Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator-dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide towards appropriate cancer care. Indeed, the need for applying machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations – such as segmentation, co-registration, classification, and dimensionality reduction – and multi-omics data integration.

This Special Issue will provide a forum to publish original research papers covering state-of-the-art and novel algorithms, methodologies, and applications of computational methods for oncological image analysis, ranging from radiogenomics to deep learning.

Dr. Leonardo Rundo
Dr. Carmelo Militello
Dr. Vincenzo Conti
Dr. Fulvio Zaccagna
Dr. Changhee Han
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 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

  • biomedical image analysis and processing
  • oncological imaging
  • computer-assisted tumor detection and segmentation
  • tumor classification
  • therapy response prediction
  • radiomics
  • radiogenomics
  • artificial intelligence
  • applied machine learning
  • precision medicine

Published Papers (8 papers)

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Research

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Article
A Computational Study on Temperature Variations in MRgFUS Treatments Using PRF Thermometry Techniques and Optical Probes
J. Imaging 2021, 7(4), 63; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7040063 - 25 Mar 2021
Viewed by 480
Abstract
Structural and metabolic imaging are fundamental for diagnosis, treatment and follow-up in oncology. Beyond the well-established diagnostic imaging applications, ultrasounds are currently emerging in the clinical practice as a noninvasive technology for therapy. Indeed, the sound waves can be used to increase the [...] Read more.
Structural and metabolic imaging are fundamental for diagnosis, treatment and follow-up in oncology. Beyond the well-established diagnostic imaging applications, ultrasounds are currently emerging in the clinical practice as a noninvasive technology for therapy. Indeed, the sound waves can be used to increase the temperature inside the target solid tumors, leading to apoptosis or necrosis of neoplastic tissues. The Magnetic resonance-guided focused ultrasound surgery (MRgFUS) technology represents a valid application of this ultrasound property, mainly used in oncology and neurology. In this paper; patient safety during MRgFUS treatments was investigated by a series of experiments in a tissue-mimicking phantom and performing ex vivo skin samples, to promptly identify unwanted temperature rises. The acquired MR images, used to evaluate the temperature in the treated areas, were analyzed to compare classical proton resonance frequency (PRF) shift techniques and referenceless thermometry methods to accurately assess the temperature variations. We exploited radial basis function (RBF) neural networks for referenceless thermometry and compared the results against interferometric optical fiber measurements. The experimental measurements were obtained using a set of interferometric optical fibers aimed at quantifying temperature variations directly in the sonication areas. The temperature increases during the treatment were not accurately detected by MRI-based referenceless thermometry methods, and more sensitive measurement systems, such as optical fibers, would be required. In-depth studies about these aspects are needed to monitor temperature and improve safety during MRgFUS treatments. Full article
(This article belongs to the Special Issue Advanced Computational Methods for Oncological Image Analysis)
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Article
Incoherent Radar Imaging for Breast Cancer Detection and Experimental Validation against 3D Multimodal Breast Phantoms
J. Imaging 2021, 7(2), 23; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7020023 - 01 Feb 2021
Viewed by 379
Abstract
In this paper we consider radar approaches for breast cancer detection. The aim is to give a brief review of the main features of incoherent methods, based on beam-forming and Multiple SIgnal Classification (MUSIC) algorithms, that we have recently developed, and to compare [...] Read more.
In this paper we consider radar approaches for breast cancer detection. The aim is to give a brief review of the main features of incoherent methods, based on beam-forming and Multiple SIgnal Classification (MUSIC) algorithms, that we have recently developed, and to compare them with classical coherent beam-forming. Those methods have the remarkable advantage of not requiring antenna characterization/compensation, which can be problematic in view of the close (to the breast) proximity set-up usually employed in breast imaging. Moreover, we proceed to an experimental validation of one of the incoherent methods, i.e., the I-MUSIC, using the multimodal breast phantom we have previously developed. While in a previous paper we focused on the phantom manufacture and characterization, here we are mainly concerned with providing the detail of the reconstruction algorithm, in particular for a new multi-step clutter rejection method that was employed and only barely described. In this regard, this contribution can be considered as a completion of our previous study. The experiments against the phantom show promising results and highlight the crucial role played by the clutter rejection procedure. Full article
(This article belongs to the Special Issue Advanced Computational Methods for Oncological Image Analysis)
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Article
Enhanced Region Growing for Brain Tumor MR Image Segmentation
J. Imaging 2021, 7(2), 22; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7020022 - 01 Feb 2021
Viewed by 456
Abstract
A brain tumor is one of the foremost reasons for the rise in mortality among children and adults. A brain tumor is a mass of tissue that propagates out of control of the normal forces that regulate growth inside the brain. A brain [...] Read more.
A brain tumor is one of the foremost reasons for the rise in mortality among children and adults. A brain tumor is a mass of tissue that propagates out of control of the normal forces that regulate growth inside the brain. A brain tumor appears when one type of cell changes from its normal characteristics and grows and multiplies abnormally. The unusual growth of cells within the brain or inside the skull, which can be cancerous or non-cancerous has been the reason for the death of adults in developed countries and children in under developing countries like Ethiopia. The studies have shown that the region growing algorithm initializes the seed point either manually or semi-manually which as a result affects the segmentation result. However, in this paper, we proposed an enhanced region-growing algorithm for the automatic seed point initialization. The proposed approach’s performance was compared with the state-of-the-art deep learning algorithms using the common dataset, BRATS2015. In the proposed approach, we applied a thresholding technique to strip the skull from each input brain image. After the skull is stripped the brain image is divided into 8 blocks. Then, for each block, we computed the mean intensities and from which the five blocks with maximum mean intensities were selected out of the eight blocks. Next, the five maximum mean intensities were used as a seed point for the region growing algorithm separately and obtained five different regions of interest (ROIs) for each skull stripped input brain image. The five ROIs generated using the proposed approach were evaluated using dice similarity score (DSS), intersection over union (IoU), and accuracy (Acc) against the ground truth (GT), and the best region of interest is selected as a final ROI. Finally, the final ROI was compared with different state-of-the-art deep learning algorithms and region-based segmentation algorithms in terms of DSS. Our proposed approach was validated in three different experimental setups. In the first experimental setup where 15 randomly selected brain images were used for testing and achieved a DSS value of 0.89. In the second and third experimental setups, the proposed approach scored a DSS value of 0.90 and 0.80 for 12 randomly selected and 800 brain images respectively. The average DSS value for the three experimental setups was 0.86. Full article
(This article belongs to the Special Issue Advanced Computational Methods for Oncological Image Analysis)
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Article
Evaluating Performance of Microwave Image Reconstruction Algorithms: Extracting Tissue Types with Segmentation Using Machine Learning
J. Imaging 2021, 7(1), 5; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7010005 - 07 Jan 2021
Cited by 1 | Viewed by 928
Abstract
Evaluating the quality of reconstructed images requires consistent approaches to extracting information and applying metrics. Partitioning medical images into tissue types permits the quantitative assessment of regions that contain a specific tissue. The assessment facilitates the evaluation of an imaging algorithm in terms [...] Read more.
Evaluating the quality of reconstructed images requires consistent approaches to extracting information and applying metrics. Partitioning medical images into tissue types permits the quantitative assessment of regions that contain a specific tissue. The assessment facilitates the evaluation of an imaging algorithm in terms of its ability to reconstruct the properties of various tissue types and identify anomalies. Microwave tomography is an imaging modality that is model-based and reconstructs an approximation of the actual internal spatial distribution of the dielectric properties of a breast over a reconstruction model consisting of discrete elements. The breast tissue types are characterized by their dielectric properties, so the complex permittivity profile that is reconstructed may be used to distinguish different tissue types. This manuscript presents a robust and flexible medical image segmentation technique to partition microwave breast images into tissue types in order to facilitate the evaluation of image quality. The approach combines an unsupervised machine learning method with statistical techniques. The key advantage for using the algorithm over other approaches, such as a threshold-based segmentation method, is that it supports this quantitative analysis without prior assumptions such as knowledge of the expected dielectric property values that characterize each tissue type. Moreover, it can be used for scenarios where there is a scarcity of data available for supervised learning. Microwave images are formed by solving an inverse scattering problem that is severely ill-posed, which has a significant impact on image quality. A number of strategies have been developed to alleviate the ill-posedness of the inverse scattering problem. The degree of success of each strategy varies, leading to reconstructions that have a wide range of image quality. A requirement for the segmentation technique is the ability to partition tissue types over a range of image qualities, which is demonstrated in the first part of the paper. The segmentation of images into regions of interest corresponding to various tissue types leads to the decomposition of the breast interior into disjoint tissue masks. An array of region and distance-based metrics are applied to compare masks extracted from reconstructed images and ground truth models. The quantitative results reveal the accuracy with which the geometric and dielectric properties are reconstructed. The incorporation of the segmentation that results in a framework that effectively furnishes the quantitative assessment of regions that contain a specific tissue is also demonstrated. The algorithm is applied to reconstructed microwave images derived from breasts with various densities and tissue distributions to demonstrate the flexibility of the algorithm and that it is not data-specific. The potential for using the algorithm to assist in diagnosis is exhibited with a tumor tracking example. This example also establishes the usefulness of the approach in evaluating the performance of the reconstruction algorithm in terms of its sensitivity and specificity to malignant tissue and its ability to accurately reconstruct malignant tissue. Full article
(This article belongs to the Special Issue Advanced Computational Methods for Oncological Image Analysis)
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Article
3D Non-Local Neural Network: A Non-Invasive Biomarker for Immunotherapy Treatment Outcome Prediction. Case-Study: Metastatic Urothelial Carcinoma
J. Imaging 2020, 6(12), 133; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6120133 - 03 Dec 2020
Viewed by 695
Abstract
Immunotherapy is regarded as one of the most significant breakthroughs in cancer treatment. Unfortunately, only a small percentage of patients respond properly to the treatment. Moreover, to date, there are no efficient bio-markers able to early discriminate the patients eligible for this treatment. [...] Read more.
Immunotherapy is regarded as one of the most significant breakthroughs in cancer treatment. Unfortunately, only a small percentage of patients respond properly to the treatment. Moreover, to date, there are no efficient bio-markers able to early discriminate the patients eligible for this treatment. In order to help overcome these limitations, an innovative non-invasive deep pipeline, integrating Computed Tomography (CT) imaging, is investigated for the prediction of a response to immunotherapy treatment. We report preliminary results collected as part of a case study in which we validated the implemented method on a clinical dataset of patients affected by Metastatic Urothelial Carcinoma. The proposed pipeline aims to discriminate patients with high chances of response from those with disease progression. Specifically, the authors propose ad-hoc 3D Deep Networks integrating Self-Attention mechanisms in order to estimate the immunotherapy treatment response from CT-scan images and such hemato-chemical data of the patients. The performance evaluation (average accuracy close to 92%) confirms the effectiveness of the proposed approach as an immunotherapy treatment response biomarker. Full article
(This article belongs to the Special Issue Advanced Computational Methods for Oncological Image Analysis)
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Article
Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection
J. Imaging 2020, 6(12), 129; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6120129 - 26 Nov 2020
Cited by 5 | Viewed by 663
Abstract
Malignant melanoma is the deadliest form of skin cancer and, in recent years, is rapidly growing in terms of the incidence worldwide rate. The most effective approach to targeted treatment is early diagnosis. Deep learning algorithms, specifically convolutional neural networks, represent a methodology [...] Read more.
Malignant melanoma is the deadliest form of skin cancer and, in recent years, is rapidly growing in terms of the incidence worldwide rate. The most effective approach to targeted treatment is early diagnosis. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for the image analysis and representation. They optimize the features design task, essential for an automatic approach on different types of images, including medical. In this paper, we adopted pretrained deep convolutional neural networks architectures for the image representation with purpose to predict skin lesion melanoma. Firstly, we applied a transfer learning approach to extract image features. Secondly, we adopted the transferred learning features inside an ensemble classification context. Specifically, the framework trains individual classifiers on balanced subspaces and combines the provided predictions through statistical measures. Experimental phase on datasets of skin lesion images is performed and results obtained show the effectiveness of the proposed approach with respect to state-of-the-art competitors. Full article
(This article belongs to the Special Issue Advanced Computational Methods for Oncological Image Analysis)
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Review

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Review
Breast Cancer Risk Assessment: A Review on Mammography-Based Approaches
J. Imaging 2021, 7(6), 98; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7060098 - 12 Jun 2021
Viewed by 242
Abstract
Breast cancer affects thousands of women across the world, every year. Methods to predict risk of breast cancer, or to stratify women in different risk levels, could help to achieve an early diagnosis, and consequently a reduction of mortality. This paper aims to [...] Read more.
Breast cancer affects thousands of women across the world, every year. Methods to predict risk of breast cancer, or to stratify women in different risk levels, could help to achieve an early diagnosis, and consequently a reduction of mortality. This paper aims to review articles that extracted texture features from mammograms and used those features along with machine learning algorithms to assess breast cancer risk. Besides that, deep learning methodologies that aimed for the same goal were also reviewed. In this work, first, a brief introduction to breast cancer statistics and screening programs is presented; after that, research done in the field of breast cancer risk assessment are analyzed, in terms of both methodologies used and results obtained. Finally, considerations about the analyzed papers are conducted. The results of this review allow to conclude that both machine and deep learning methodologies provide promising results in the field of risk analysis, either in a stratification in risk groups, or in a prediction of a risk score. Although promising, future endeavors in this field should consider the possibility of the implementation of the methodology in clinical practice. Full article
(This article belongs to the Special Issue Advanced Computational Methods for Oncological Image Analysis)
Review
Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art
J. Imaging 2021, 7(2), 19; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7020019 - 29 Jan 2021
Viewed by 799
Abstract
Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate [...] Read more.
Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis. Full article
(This article belongs to the Special Issue Advanced Computational Methods for Oncological Image Analysis)
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