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Zhou X, Tong T, Zhong Z, Fan H, Li Z. Saliency-CCE: Exploiting colour contextual extractor and saliency-based biomedical image segmentation. Comput Biol Med 2023; 154:106551. [PMID: 36716685 DOI: 10.1016/j.compbiomed.2023.106551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 01/03/2023] [Accepted: 01/11/2023] [Indexed: 01/21/2023]
Abstract
Biomedical image segmentation is one critical component in computer-aided system diagnosis. However, various non-automatic segmentation methods are usually designed to segment target objects with single-task driven, ignoring the potential contribution of multi-task, such as the salient object detection (SOD) task and the image segmentation task. In this paper, we propose a novel dual-task framework for white blood cell (WBC) and skin lesion (SL) saliency detection and segmentation in biomedical images, called Saliency-CCE. Saliency-CCE consists of a preprocessing of hair removal for skin lesions images, a novel colour contextual extractor (CCE) module for the SOD task and an improved adaptive threshold (AT) paradigm for the image segmentation task. In the SOD task, we perform the CCE module to extract hand-crafted features through a novel colour channel volume (CCV) block and a novel colour activation mapping (CAM) block. We first exploit the CCV block to generate a target object's region of interest (ROI). After that, we employ the CAM block to yield a refined salient map as the final salient map from the extracted ROI. We propose a novel adaptive threshold (AT) strategy in the segmentation task to automatically segment the WBC and SL from the final salient map. We evaluate our proposed Saliency-CCE on the ISIC-2016, the ISIC-2017, and the SCISC datasets, which outperform representative state-of-the-art SOD and biomedical image segmentation approaches. Our code is available at https://github.com/zxg3017/Saliency-CCE.
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Affiliation(s)
- Xiaogen Zhou
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, P.R. China; College of Physics and Information Engineering, Fuzhou University, Fuzhou, P.R. China
| | - Tong Tong
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, P.R. China
| | - Zhixiong Zhong
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, P.R. China
| | - Haoyi Fan
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, P.R. China
| | - Zuoyong Li
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, P.R. China.
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2
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A Deep Model of Visual Attention for Saliency Detection on 3D Objects. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11180-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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3
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Using CNN Saliency Maps and EEG Modulation Spectra for Improved and More Interpretable Machine Learning-Based Alzheimer's Disease Diagnosis. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:3198066. [PMID: 36818579 PMCID: PMC9931465 DOI: 10.1155/2023/3198066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 09/15/2022] [Accepted: 01/11/2023] [Indexed: 02/11/2023]
Abstract
Biomarkers based on resting-state electroencephalography (EEG) signals have emerged as a promising tool in the study of Alzheimer's disease (AD). Recently, a state-of-the-art biomarker was found based on visual inspection of power modulation spectrograms where three "patches" or regions from the modulation spectrogram were proposed and used for AD diagnostics. Here, we propose the use of deep neural networks, in particular convolutional neural networks (CNNs) combined with saliency maps, trained on power modulation spectrogram inputs to find optimal patches in a data-driven manner. Experiments are conducted on EEG data collected from fifty-four participants, including 20 healthy controls, 19 patients with mild AD, and 15 moderate-to-severe AD patients. Five classification tasks are explored, including the three-class problem, early-stage detection (control vs. mild-AD), and severity level detection (mild vs. moderate-to-severe). Experimental results show the proposed biomarkers outperform the state-of-the-art benchmark across all five tasks, as well as finding complementary modulation spectrogram regions not previously seen via visual inspection. Lastly, experiments are conducted on the proposed biomarkers to test their sensitivity to age, as this is a known confound in AD characterization. Across all five tasks, none of the proposed biomarkers showed a significant relationship with age, thus further highlighting their usefulness for automated AD diagnostics.
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4
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Chen X, Lei Y, Su J, Yang H, Ni W, Yu J, Gu Y, Mao Y. A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges. Curr Neuropharmacol 2022; 20:1359-1382. [PMID: 34749621 PMCID: PMC9881077 DOI: 10.2174/1570159x19666211108141446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/07/2021] [Accepted: 10/10/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND A variety of emerging medical imaging technologies based on artificial intelligence have been widely applied in many diseases, but they are still limitedly used in the cerebrovascular field even though the diseases can lead to catastrophic consequences. OBJECTIVE This work aims to discuss the current challenges and future directions of artificial intelligence technology in cerebrovascular diseases through reviewing the existing literature related to applications in terms of computer-aided detection, prediction and treatment of cerebrovascular diseases. METHODS Based on artificial intelligence applications in four representative cerebrovascular diseases including intracranial aneurysm, arteriovenous malformation, arteriosclerosis and moyamoya disease, this paper systematically reviews studies published between 2006 and 2021 in five databases: National Center for Biotechnology Information, Elsevier Science Direct, IEEE Xplore Digital Library, Web of Science and Springer Link. And three refinement steps were further conducted after identifying relevant literature from these databases. RESULTS For the popular research topic, most of the included publications involved computer-aided detection and prediction of aneurysms, while studies about arteriovenous malformation, arteriosclerosis and moyamoya disease showed an upward trend in recent years. Both conventional machine learning and deep learning algorithms were utilized in these publications, but machine learning techniques accounted for a larger proportion. CONCLUSION Algorithms related to artificial intelligence, especially deep learning, are promising tools for medical imaging analysis and will enhance the performance of computer-aided detection, prediction and treatment of cerebrovascular diseases.
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Affiliation(s)
- Xi Chen
- School of Information Science and Technology, Fudan University, Shanghai, China; ,These authors contributed equally to this work
| | - Yu Lei
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China,These authors contributed equally to this work
| | - Jiabin Su
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Heng Yang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Wei Ni
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, Shanghai, China; ,Address correspondence to these authors at the School of Information Science and Technology, Fudan University, Shanghai 200433, China; Tel: +86 021 65643202; Fax: +86 021 65643202; E-mail: Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai 200040, China; Tel: +86 021 52889999; Fax: +86 021 62489191; E-mail:
| | - Yuxiang Gu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China,Address correspondence to these authors at the School of Information Science and Technology, Fudan University, Shanghai 200433, China; Tel: +86 021 65643202; Fax: +86 021 65643202; E-mail: Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai 200040, China; Tel: +86 021 52889999; Fax: +86 021 62489191; E-mail:
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
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Validation of a Saliency Map for Assessing Image Quality in Nuclear Medicine: Experimental Study Outcomes. RADIATION 2022. [DOI: 10.3390/radiation2030018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Recently, the use of saliency maps to evaluate the image quality of nuclear medicine images has been reported. However, that study only compared qualitative visual evaluations and did not perform a quantitative assessment. The study’s aim was to demonstrate the possibility of using saliency maps (calculated from intensity and flicker) to assess nuclear medicine image quality by comparison with the evaluator’s gaze data obtained from an eye-tracking device. We created 972 positron emission tomography images by changing the position of the hot sphere, imaging time, and number of iterations in the iterative reconstructions. Pearson’s correlation coefficient between the saliency map calculated from each image and the evaluator’s gaze data during image presentation was calculated. A strong correlation (r ≥ 0.94) was observed between the saliency map (intensity) and the evaluator’s gaze data. This trend was also observed in images obtained from a clinical device. For short acquisition times, the gaze to the hot sphere position was higher for images with fewer iterations during the iterative reconstruction. However, no differences in iterations were found when the acquisition time increased. Saliency by flicker could be applied to clinical images without preprocessing, although compared with the gaze image, it increased slowly.
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Ramana K, Kumar MR, Sreenivasulu K, Gadekallu TR, Bhatia S, Agarwal P, Idrees SM. Early Prediction of Lung Cancers Using Deep Saliency Capsule and Pre-Trained Deep Learning Frameworks. Front Oncol 2022; 12:886739. [PMID: 35785184 PMCID: PMC9247339 DOI: 10.3389/fonc.2022.886739] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/13/2022] [Indexed: 12/12/2022] Open
Abstract
Lung cancer is the cellular fission of abnormal cells inside the lungs that leads to 72% of total deaths worldwide. Lung cancer are also recognized to be one of the leading causes of mortality, with a chance of survival of only 19%. Tumors can be diagnosed using a variety of procedures, including X-rays, CT scans, biopsies, and PET-CT scans. From the above techniques, Computer Tomography (CT) scan technique is considered to be one of the most powerful tools for an early diagnosis of lung cancers. Recently, machine and deep learning algorithms have picked up peak energy, and this aids in building a strong diagnosis and prediction system using CT scan images. But achieving the best performances in diagnosis still remains on the darker side of the research. To solve this problem, this paper proposes novel saliency-based capsule networks for better segmentation and employs the optimized pre-trained transfer learning for the better prediction of lung cancers from the input CT images. The integration of capsule-based saliency segmentation leads to the reduction and eventually reduces the risk of computational complexity and overfitting problem. Additionally, hyperparameters of pretrained networks are tuned by the whale optimization algorithm to improve the prediction accuracy by sacrificing the complexity. The extensive experimentation carried out using the LUNA-16 and LIDC Lung Image datasets and various performance metrics such as accuracy, precision, recall, specificity, and F1-score are evaluated and analyzed. Experimental results demonstrate that the proposed framework has achieved the peak performance of 98.5% accuracy, 99.0% precision, 98.8% recall, and 99.1% F1-score and outperformed the DenseNet, AlexNet, Resnets-50, Resnets-100, VGG-16, and Inception models.
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Affiliation(s)
- Kadiyala Ramana
- Department of Information Technology (IT), Chaitanya Bharathi Institute of Technology, Hyderabad, India
| | - Madapuri Rudra Kumar
- Department of Computer Science and Engineering (CSE), G. Pullaiah College of Engineering and Technology, Kurnool, India
| | - K. Sreenivasulu
- Department of Computer Science and Engineering (CSE), G. Pullaiah College of Engineering and Technology, Kurnool, India
| | | | - Surbhi Bhatia
- Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al Hasa, Saudi Arabia
| | - Parul Agarwal
- Department of Computer Science and Engineering (CSE), Jamia Hamdard, India
| | - Sheikh Mohammad Idrees
- Department of Computer Science Institutt for datateknologi og informatikk (IDI), Norwegian University of Science and Technology, Gjøvik, Norway
- *Correspondence: Sheikh Mohammad Idrees,
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Lather M, Singh P. Tumor segmentation from brain MR images using STSA based modified K-means clustering approach. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Due to the complexity of the task involved in extracting and segmenting the tumor area from the images, it is very challenging to be successful in detecting the disorders. This paper presents a method that can handle the various issues related to brain tumor segmentation, such as noise reduction, artifact removal, and visual interpretation. In this paper, an advanced brain tumor segmentation approach is proposed that is working in different phases such as pre-processing that includes image enhancement and noise removal from the input image, Stationary Wavelet Transform (SWT) based feature extraction and Sine Tree-Seed Algorithm (STSA) based modified K-means clustering algorithm for segmentation. In addition to this, the proposed approach is analyzed for its effectiveness by considering the impact of Gaussian and speckle noise on the original image. The experimental results have been evaluated in three different cases of the input noise in terms of accuracy, precision, recall, F-score, and Jaccard. Finally, a comparative analysis is performed with different conventional approaches to prove the effectiveness of the proposed scheme. The result analysis shows an improvement of approximately 1% in terms of accuracy, 4%, and 5% in terms of precision and recall when compared to the other techniques.
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Affiliation(s)
- Mansi Lather
- Department of Computer Science and Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat, India
| | - Parvinder Singh
- Department of Computer Science and Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat, India
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Gore S, Chougule T, Jagtap J, Saini J, Ingalhalikar M. A Review of Radiomics and Deep Predictive Modeling in Glioma Characterization. Acad Radiol 2021; 28:1599-1621. [PMID: 32660755 DOI: 10.1016/j.acra.2020.06.016] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 06/11/2020] [Accepted: 06/11/2020] [Indexed: 12/22/2022]
Abstract
Recent developments in glioma categorization based on biological genotypes and application of computational machine learning or deep learning based predictive models using multi-modal MRI biomarkers to assess these genotypes provides potential assurance for optimal and personalized treatment plans and efficacy. Artificial intelligence based quantified assessment of glioma using MRI derived hand-crafted or auto-extracted features have become crucial as genomic alterations can be associated with MRI based phenotypes. This survey integrates all the recent work carried out in state-of-the-art radiomics, and Artificial Intelligence based learning solutions related to molecular diagnosis, prognosis, and treatment monitoring with the aim to create a structured resource on radiogenomic analysis of glioma. Challenges such as inter-scanner variability, requirement of benchmark datasets, prospective validations for clinical applicability are discussed with further scope for designing optimal solutions for glioma stratification with immediate recommendations for further diagnostic decisions and personalized treatment plans for glioma patients.
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Uthra Devi K, Gomathi R. Convolutional Neural Network Based Brain Tumor Classification Using Robust Background Saliency Detection. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
To perceive the tumors found in brain and their treatment, experts manually note and identify different Regions of Interest (ROI). To overcome the faults and divergences during this state, automated analysis is performed. A unique technique is used to classify the tumor section of the
brain from an MRI is proposed using saliency-focused image depiction and optimization in classification based on CNN. Primarily, the MRI images are pre-processed using the Canny Edge Finding algorithm and then those images are represented as saliency driven based on Robust Background Saliency
Detection (RBD). Followed by the abstraction of features then classifying the image is performed using CNN along with ADAM optimization. The implementation is accomplished, and the results are analyzed, showing that it outperforms previous techniques.
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Affiliation(s)
- K. Uthra Devi
- Department of Information Technology, Indra Ganesan College of Engineering, Trichy 620012, TamilNadu, India
| | - R. Gomathi
- Department of Electronics and Communication Engineering, University College of Engineering-Dindigul Campus, Dindigul 624622, TamilNadu, India
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Gryska E, Schneiderman J, Björkman-Burtscher I, Heckemann RA. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ Open 2021; 11:e042660. [PMID: 33514580 PMCID: PMC7849889 DOI: 10.1136/bmjopen-2020-042660] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 01/09/2021] [Accepted: 01/12/2021] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVES Medical image analysis practices face challenges that can potentially be addressed with algorithm-based segmentation tools. In this study, we map the field of automatic MR brain lesion segmentation to understand the clinical applicability of prevalent methods and study designs, as well as challenges and limitations in the field. DESIGN Scoping review. SETTING Three databases (PubMed, IEEE Xplore and Scopus) were searched with tailored queries. Studies were included based on predefined criteria. Emerging themes during consecutive title, abstract, methods and whole-text screening were identified. The full-text analysis focused on materials, preprocessing, performance evaluation and comparison. RESULTS Out of 2990 unique articles identified through the search, 441 articles met the eligibility criteria, with an estimated growth rate of 10% per year. We present a general overview and trends in the field with regard to publication sources, segmentation principles used and types of lesions. Algorithms are predominantly evaluated by measuring the agreement of segmentation results with a trusted reference. Few articles describe measures of clinical validity. CONCLUSIONS The observed reporting practices leave room for improvement with a view to studying replication, method comparison and clinical applicability. To promote this improvement, we propose a list of recommendations for future studies in the field.
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Affiliation(s)
- Emilia Gryska
- Medical Radiation Sciences, Goteborgs universitet Institutionen for kliniska vetenskaper, Goteborg, Sweden
| | - Justin Schneiderman
- Sektionen för klinisk neurovetenskap, Goteborgs Universitet Institutionen for Neurovetenskap och fysiologi, Goteborg, Sweden
| | | | - Rolf A Heckemann
- Medical Radiation Sciences, Goteborgs universitet Institutionen for kliniska vetenskaper, Goteborg, Sweden
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12
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Banerjee S, Mitra S. Novel Volumetric Sub-region Segmentation in Brain Tumors. Front Comput Neurosci 2020; 14:3. [PMID: 32038216 PMCID: PMC6993215 DOI: 10.3389/fncom.2020.00003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 01/08/2020] [Indexed: 11/13/2022] Open
Abstract
A novel deep learning based model called Multi-Planar Spatial Convolutional Neural Network (MPS-CNN) is proposed for effective, automated segmentation of different sub-regions viz. peritumoral edema (ED), necrotic core (NCR), enhancing and non-enhancing tumor core (ET/NET), from multi-modal MR images of the brain. An encoder-decoder type CNN model is designed for pixel-wise segmentation of the tumor along three anatomical planes (axial, sagittal, and coronal) at the slice level. These are then combined, by incorporating a consensus fusion strategy with a fully connected Conditional Random Field (CRF) based post-refinement, to produce the final volumetric segmentation of the tumor and its constituent sub-regions. Concepts, such as spatial-pooling and unpooling are used to preserve the spatial locations of the edge pixels, for reducing segmentation error around the boundaries. A new aggregated loss function is also developed for effectively handling data imbalance. The MPS-CNN is trained and validated on the recent Multimodal Brain Tumor Segmentation Challenge (BraTS) 2018 dataset. The Dice scores obtained for the validation set for whole tumor (WT :NCR/NE +ET +ED), tumor core (TC:NCR/NET +ET), and enhancing tumor (ET) are 0.90216, 0.87247, and 0.82445. The proposed MPS-CNN is found to perform the best (based on leaderboard scores) for ET and TC segmentation tasks, in terms of both the quantitative measures (viz. Dice and Hausdorff). In case of the WT segmentation it also achieved the second highest accuracy, with a score which was only 1% less than that of the best performing method.
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Affiliation(s)
- Subhashis Banerjee
- Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India
- Department of CSE, University of Calcutta, Kolkata, India
| | - Sushmita Mitra
- Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India
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13
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Bodalal Z, Trebeschi S, Nguyen-Kim TDL, Schats W, Beets-Tan R. Radiogenomics: bridging imaging and genomics. Abdom Radiol (NY) 2019; 44:1960-1984. [PMID: 31049614 DOI: 10.1007/s00261-019-02028-w] [Citation(s) in RCA: 165] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
From diagnostics to prognosis to response prediction, new applications for radiomics are rapidly being developed. One of the fastest evolving branches involves linking imaging phenotypes to the tumor genetic profile, a field commonly referred to as "radiogenomics." In this review, a general outline of radiogenomic literature concerning prominent mutations across different tumor sites will be provided. The field of radiogenomics originates from image processing techniques developed decades ago; however, many technical and clinical challenges still need to be addressed. Nevertheless, increasingly accurate and robust radiogenomic models are being presented and the future appears to be bright.
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14
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Carass A, Cuzzocreo JL, Han S, Hernandez-Castillo CR, Rasser PE, Ganz M, Beliveau V, Dolz J, Ben Ayed I, Desrosiers C, Thyreau B, Romero JE, Coupé P, Manjón JV, Fonov VS, Collins DL, Ying SH, Onyike CU, Crocetti D, Landman BA, Mostofsky SH, Thompson PM, Prince JL. Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images. Neuroimage 2018; 183:150-172. [PMID: 30099076 PMCID: PMC6271471 DOI: 10.1016/j.neuroimage.2018.08.003] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Revised: 08/03/2018] [Accepted: 08/03/2018] [Indexed: 01/26/2023] Open
Abstract
The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is important in the study of several neurological diseases including cerebellar ataxia, autism, and schizophrenia. Different structural subdivisions of the cerebellum have been shown to correlate with differing pathologies. To further understand these pathologies, it is helpful to automatically parcellate the cerebellum at the highest fidelity possible. In this paper, we coordinated with colleagues around the world to evaluate automated cerebellum parcellation algorithms on two clinical cohorts showing that the cerebellum can be parcellated to a high accuracy by newer methods. We characterize these various methods at four hierarchical levels: coarse (i.e., whole cerebellum and gross structures), lobe, subdivisions of the vermis, and the lobules. Due to the number of labels, the hierarchy of labels, the number of algorithms, and the two cohorts, we have restricted our analyses to the Dice measure of overlap. Under these conditions, machine learning based methods provide a collection of strategies that are efficient and deliver parcellations of a high standard across both cohorts, surpassing previous work in the area. In conjunction with the rank-sum computation, we identified an overall winning method.
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Affiliation(s)
- Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD, 21218, USA.
| | - Jennifer L Cuzzocreo
- Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA
| | - Shuo Han
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, 20892, USA
| | - Carlos R Hernandez-Castillo
- Consejo Nacional de Ciencia y Tecnología, Instituto de Neuroetología, Universidad Veracruzana, Xalapa, Mexico
| | - Paul E Rasser
- Priority Research Centre for Brain & Mental Health and Stroke & Brain Injury, University of Newcastle, Callaghan, NSW, Australia
| | - Melanie Ganz
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Vincent Beliveau
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jose Dolz
- Laboratory for Imagery, Vision, and Artificial Intelligence, École de Technologie Supérieure, Montreal, QC, Canada
| | - Ismail Ben Ayed
- Laboratory for Imagery, Vision, and Artificial Intelligence, École de Technologie Supérieure, Montreal, QC, Canada
| | - Christian Desrosiers
- Laboratory for Imagery, Vision, and Artificial Intelligence, École de Technologie Supérieure, Montreal, QC, Canada
| | - Benjamin Thyreau
- Institute of Development, Aging and Cancer, Tohoku University, Japan
| | - José E Romero
- Instituto Universitario de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain
| | - Pierrick Coupé
- University of Bordeaux, LaBRI, UMR 5800, PICTURA, Talence, F-33400, France; CNRS, LaBRI, UMR 5800, PICTURA, Talence, F-33400, France
| | - José V Manjón
- Instituto Universitario de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain
| | - Vladimir S Fonov
- Image Processing Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - D Louis Collins
- Image Processing Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Sarah H Ying
- Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA
| | - Chiadi U Onyike
- Department of Psychiatry and Behavioral Sciences, The Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA
| | - Deana Crocetti
- Center for Neurodevelopmental Medicine and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, 21205, USA
| | - Bennett A Landman
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, 37235, USA
| | - Stewart H Mostofsky
- Center for Neurodevelopmental Medicine and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, 21205, USA; Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA; Department of Psychiatry and Behavioral Sciences, The Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, 90292, USA; Departments of Neurology, Pediatrics, Psychiatry, Radiology, Engineering, and Ophthalmology, University of Southern California, Los Angeles, CA, 90033, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD, 21218, USA
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15
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Mitra S, Banerjee S, Hayashi Y. Volumetric brain tumour detection from MRI using visual saliency. PLoS One 2017; 12:e0187209. [PMID: 29095877 PMCID: PMC5667735 DOI: 10.1371/journal.pone.0187209] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Accepted: 10/16/2017] [Indexed: 11/19/2022] Open
Abstract
Medical image processing has become a major player in the world of automatic tumour region detection and is tantamount to the incipient stages of computer aided design. Saliency detection is a crucial application of medical image processing, and serves in its potential aid to medical practitioners by making the affected area stand out in the foreground from the rest of the background image. The algorithm developed here is a new approach to the detection of saliency in a three dimensional multi channel MR image sequence for the glioblastoma multiforme (a form of malignant brain tumour). First we enhance the three channels, FLAIR (Fluid Attenuated Inversion Recovery), T2 and T1C (contrast enhanced with gadolinium) to generate a pseudo coloured RGB image. This is then converted to the CIE L*a*b* color space. Processing on cubes of sizes k = 4, 8, 16, the L*a*b* 3D image is then compressed into volumetric units; each representing the neighbourhood information of the surrounding 64 voxels for k = 4, 512 voxels for k = 8 and 4096 voxels for k = 16, respectively. The spatial distance of these voxels are then compared along the three major axes to generate the novel 3D saliency map of a 3D image, which unambiguously highlights the tumour region. The algorithm operates along the three major axes to maximise the computation efficiency while minimising loss of valuable 3D information. Thus the 3D multichannel MR image saliency detection algorithm is useful in generating a uniform and logistically correct 3D saliency map with pragmatic applicability in Computer Aided Detection (CADe). Assignment of uniform importance to all three axes proves to be an important factor in volumetric processing, which helps in noise reduction and reduces the possibility of compromising essential information. The effectiveness of the algorithm was evaluated over the BRATS MICCAI 2015 dataset having 274 glioma cases, consisting both of high grade and low grade GBM. The results were compared with that of the 2D saliency detection algorithm taken over the entire sequence of brain data. For all comparisons, the Area Under the receiver operator characteristic (ROC) Curve (AUC) has been found to be more than 0.99 ± 0.01 over various tumour types, structures and locations.
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Affiliation(s)
- Somosmita Mitra
- Department of Computer Science and Engineering, Institute of Engineering & Management, Kolkata 700091, West Bengal, India
- * E-mail:
| | - Subhashis Banerjee
- Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700108, West Bengal, India
- Department of Computer Science and Engineering, University of Calcutta, Kolkata 700106, West Bengal, India
| | - Yoichi Hayashi
- Dept. of Computer Science, Meiji University, Tama-ku, Kawasaki 214-8571, Japan
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16
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Rangrej SB, Sivaswamy J. Assistive lesion-emphasis system: an assistive system for fundus image readers. J Med Imaging (Bellingham) 2017; 4:024503. [PMID: 28560245 PMCID: PMC5443420 DOI: 10.1117/1.jmi.4.2.024503] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 05/08/2017] [Indexed: 11/14/2022] Open
Abstract
Computer-assisted diagnostic (CAD) tools are of interest as they enable efficient decision-making in clinics and the screening of diseases. The traditional approach to CAD algorithm design focuses on the automated detection of abnormalities independent of the end-user, who can be an image reader or an expert. We propose a reader-centric system design wherein a reader's attention is drawn to abnormal regions in a least-obtrusive yet effective manner, using saliency-based emphasis of abnormalities and without altering the appearance of the background tissues. We present an assistive lesion-emphasis system (ALES) based on the above idea, for fundus image-based diabetic retinopathy diagnosis. Lesion-saliency is learnt using a convolutional neural network (CNN), inspired by the saliency model of Itti and Koch. The CNN is used to fine-tune standard low-level filters and learn high-level filters for deriving a lesion-saliency map, which is then used to perform lesion-emphasis via a spatially variant version of gamma correction. The proposed system has been evaluated on public datasets and benchmarked against other saliency models. It was found to outperform other saliency models by 6% to 30% and boost the contrast-to-noise ratio of lesions by more than 30%. Results of a perceptual study also underscore the effectiveness and, hence, the potential of ALES as an assistive tool for readers.
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17
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Kadkhodaei M, Samavi S, Karimi N, Mohaghegh H, Soroushmehr SMR, Ward K, All A, Najarian K. Automatic segmentation of multimodal brain tumor images based on classification of super-voxels. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2016:5945-5948. [PMID: 28269606 DOI: 10.1109/embc.2016.7592082] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Despite the rapid growth in brain tumor segmentation approaches, there are still many challenges in this field. Automatic segmentation of brain images has a critical role in decreasing the burden of manual labeling and increasing robustness of brain tumor diagnosis. We consider segmentation of glioma tumors, which have a wide variation in size, shape and appearance properties. In this paper images are enhanced and normalized to same scale in a preprocessing step. The enhanced images are then segmented based on their intensities using 3D super-voxels. Usually in images a tumor region can be regarded as a salient object. Inspired by this observation, we propose a new feature which uses a saliency detection algorithm. An edge-aware filtering technique is employed to align edges of the original image to the saliency map which enhances the boundaries of the tumor. Then, for classification of tumors in brain images, a set of robust texture features are extracted from super-voxels. Experimental results indicate that our proposed method outperforms a comparable state-of-the-art algorithm in term of dice score.
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