Automated thresholded region classification using a robust feature selection method for PET-CT

Fluorodeoxyglucose Positron Emission Tomography - Computed Tomography (FDG PET-CT) is the preferred imaging modality for staging the lymphomas. Sites of disease usually appear as foci of increased FDG uptake. Thresholding is the most common method used to identify these regions. The thresholding method, however, is not able to separate sites of FDG excretion and physiological FDG uptake (sFEPU) from sites of disease. sFEPU can make image interpretation problematic and so the ability to identify / label sFEPU will improve image interpretation and the assessment of the total disease burden and will be beneficial for any computer aided diagnosis software. Existing classification methods, however, are sub-optimal as there is a tendency for over-fitting and increased computational burden because they are unable to identify optimal features that can be used for classification. In this study, we propose a new method to delineate sFEPU from thresholded PET images. We propose a feature selection method, which differs from existing approaches, in that it focuses on selecting optimal features from individual structures, rather than from the entire image. Our classification results on 9222 coronal slices derived from 40 clinical lymphoma patient studies produced higher classification accuracy when compared to existing feature selection based methods

Stacked fully convolutional networks with multi-channel learning: application to medical image segmentation

The automated segmentation of regions of interest (ROIs) in medical imaging is the fundamental requirement for the derivation of high-level semantics for image analysis in clinical decision support systems. Traditional segmentation approaches such as region-based depend heavily upon hand-crafted features and a priori knowledge of the user. As such, these methods are difficult to adopt within a clinical environment. Recently, methods based on fully convolutional networks (FCN) have achieved great success in the segmentation of general images. FCNs leverage a large labeled dataset to hierarchically learn the features that best correspond to the shallow appearance as well as the deep semantics of the images. However, when applied to medical images, FCNs usually produce coarse ROI detection and poor boundary definitions primarily due to the limited number of labeled training data and limited constraints of label agreement among neighboring similar pixels. In this paper, we propose a new stacked FCN architecture with multi-channel learning (SFCN-ML). We embed the FCN in a stacked architecture to learn the foreground ROI features and background non-ROI features separately and then integrate these different channels to produce the final segmentation result. In contrast to traditional FCN methods, our SFCN-ML architecture enables the visual attributes and semantics derived from both the fore- and background channels to be iteratively learned and inferred. We conducted extensive experiments on three public datasets with a variety of visual challenges. Our results show that our SFCN-ML is more effective and robust than a routine FCN and its variants, and other state-of-the-art methods

Translational Applications of Artificial Intelligence and Machine Learning for Diagnostic Pathology in Lymphoid Neoplasms: A Comprehensive and Evolutive Analysis

Genomic analysis and digitalization of medical records have led to a big data scenario within hematopathology. Artificial intelligence and machine learning tools are increasingly used to integrate clinical, histopathological, and genomic data in lymphoid neoplasms. In this study, we identified global trends, cognitive, and social framework of this field from 1990 to 2020. Metadata were obtained from the Clarivate Analytics Web of Science database in January 2021. A total of 525 documents were assessed by document type, research areas, source titles, organizations, and countries. SciMAT and VOSviewer package were used to perform scientific mapping analysis. Geographical distribution showed the USA and People’s Republic of China as the most productive countries, reporting up to 190 (36.19%) of all documents. A third-degree polynomic equation predicts that future global production in this area will be three-fold the current number, near 2031. Thematically, current research is focused on the integration of digital image analysis and genomic sequencing in Non-Hodgkin lymphomas, prediction of chemotherapy response and validation of new prognostic models. These findings can serve pathology departments to depict future clinical and research avenues, but also, public institutions and administrations to promote synergies and optimize funding allocation.Andalusia Health System - RH-0145-2020EU FEDER ITI Grant for Cadiz Province PI-0032-201

Deep Networks Based Energy Models for Object Recognition from Multimodality Images

Object recognition has been extensively investigated in computer vision area, since it is a fundamental and essential technique in many important applications, such as robotics, auto-driving, automated manufacturing, and security surveillance. According to the selection criteria, object recognition mechanisms can be broadly categorized into object proposal and classification, eye fixation prediction and saliency object detection. Object proposal tends to capture all potential objects from natural images, and then classify them into predefined groups for image description and interpretation. For a given natural image, human perception is normally attracted to the most visually important regions/objects. Therefore, eye fixation prediction attempts to localize some interesting points or small regions according to human visual system (HVS). Based on these interesting points and small regions, saliency object detection algorithms propagate the important extracted information to achieve a refined segmentation of the whole salient objects. In addition to natural images, object recognition also plays a critical role in clinical practice. The informative insights of anatomy and function of human body obtained from multimodality biomedical images such as magnetic resonance imaging (MRI), transrectal ultrasound (TRUS), computed tomography (CT) and positron emission tomography (PET) facilitate the precision medicine. Automated object recognition from biomedical images empowers the non-invasive diagnosis and treatments via automated tissue segmentation, tumor detection and cancer staging. The conventional recognition methods normally utilize handcrafted features (such as oriented gradients, curvature, Haar features, Haralick texture features, Laws energy features, etc.) depending on the image modalities and object characteristics. It is challenging to have a general model for object recognition. Superior to handcrafted features, deep neural networks (DNN) can extract self-adaptive features corresponding with specific task, hence can be employed for general object recognition models. These DNN-features are adjusted semantically and cognitively by over tens of millions parameters corresponding to the mechanism of human brain, therefore leads to more accurate and robust results. Motivated by it, in this thesis, we proposed DNN-based energy models to recognize object on multimodality images. For the aim of object recognition, the major contributions of this thesis can be summarized below: 1. We firstly proposed a new comprehensive autoencoder model to recognize the position and shape of prostate from magnetic resonance images. Different from the most autoencoder-based methods, we focused on positive samples to train the model in which the extracted features all come from prostate. After that, an image energy minimization scheme was applied to further improve the recognition accuracy. The proposed model was compared with three classic classifiers (i.e. support vector machine with radial basis function kernel, random forest, and naive Bayes), and demonstrated significant superiority for prostate recognition on magnetic resonance images. We further extended the proposed autoencoder model for saliency object detection on natural images, and the experimental validation proved the accurate and robust saliency object detection results of our model. 2. A general multi-contexts combined deep neural networks (MCDN) model was then proposed for object recognition from natural images and biomedical images. Under one uniform framework, our model was performed in multi-scale manner. Our model was applied for saliency object detection from natural images as well as prostate recognition from magnetic resonance images. Our experimental validation demonstrated that the proposed model was competitive to current state-of-the-art methods. 3. We designed a novel saliency image energy to finely segment salient objects on basis of our MCDN model. The region priors were taken into account in the energy function to avoid trivial errors. Our method outperformed state-of-the-art algorithms on five benchmarking datasets. In the experiments, we also demonstrated that our proposed saliency image energy can boost the results of other conventional saliency detection methods

Computer-Aided Staging of Lymphoma Patients With FDG PET/CT Imaging Based on Textural Information

International audienceWe have designed a computer-aided diagnosis system to discriminate between hypermetabolic cancer lesions and hypermetabolic inflammatory or physiological but noncancerous processes in FDG PET/CT exams of lymphoma patients. Detection performance of the support vector machine (SVM) classifier was assessed based on feature sets including 105 positron emission tomography (PET) and Computed tomography (CT) characteristics derived from the clinical practice and from more sophisticated texture analysis. An original feature selection method based on combining different filter methods was proposed. The evaluation database consisted of 156 lymphomatous and 32 suspicious but nonlymphomatous regions of interest. Different types of training databases including either the PET and CT features or the PET features only, with or without feature selection, were evaluated to assess the added value of multimodality and texture information on classification performance. An optimization study was conducted for each classifier separately to select the best combination of parameters. Promising classification performance was achieved by the SVM classifier combined with the 12 most discriminant PET and CT features with a value of the area under the receiver operating curve of 0.91

Computer aided staging of lymphoma patients with FDG PET/CT imaging based on textural information

International audienceWe have designed a computer aided diagnosis (CADx) system to assess the presence of cancer in FDG PET/CT exams of lymphoma patients. Detection performances of the random decision forest (RDF) and support vector machine (SVM) classifiers were assessed based on a feature set including 115 PET and CT first order and textural parameters. An original feature selection method based on combining different filter methods was proposed. The evaluation database consisted of 156 lymphomatous (M for malignant), 158 physiologic (N for normal) and 32 inflammatory (NS for normal suspicious) regions of interest. An optimization study was performed for each classifier separately to select the best combination of parameters considering the two problems of discriminating the {M} and {NS+N} classes and the {M} and {NS} classes. Promising classification performance was achieved by the SVM combined with the 12 most discriminant features with AUC values of 0.97 and 0.91 for the first and second problem respectively

PET/CT Radiomics em linfomas de Hodgkin

O presente trabalho propõe-se a explorar o valor e conceito da radiomics em PET/CT/RT num estudo retrospetivo de 17 casos de Linfomas de Hodgkin pré quimioterapia (QT), após QT e após RT. O trabalho começa por abordar o estado da arte sobre a patologia (do seu diagnóstico e estadiamento ao tratamento e resultados terapêuticos esperados), assim como o emprego da PET/CT (princípios físicos e sua importância nos linfomas) e por fim o workflow radiómico (aquisição, segmentação, características, sua utilidade na clínica, tendências, desafios e limitações). De seguida expõe os materiais e métodos usados para a obtenção do radioma, acabando com a descrição e discussão dos resultados obtidos com a análise de dados e estatística orientada pela clínica. O processo radiómico que aqui se descreve é claramente determinado pelo contexto específico da radioterapia. Verificamos que os resultados obtidos são compatíveis com os resultados clínicos, estando todo o processo pronto para ser escalável a outros doentes. Apesar da dimensão reduzida da base de dados, garantimos consistência do dataset decorrente do estrito cumprimento dos protocolos clínicos e de aquisição de imagem. Relativamente aos descritores radiómicos texturais, verifica-se após QT, na modalidade PET maior homogeneidade nos doentes sem doença bulky e que responderam à QT e nos doentes sem sintomas B verificam-se poucas zonas de tamanho grande com valores altos de SUV. Nos doentes com três ou mais áreas ganglionares envolvidas verifica-se maior uniformidade e homogeneidade, sendo maior no sexo feminino. Na modalidade CT verifica-se maior homogeneidade nos doentes com resposta à QT e nos doentes sem três ou mais áreas ganglionares envolvidas diminuição das zonas de tamanho grande com valores baixos de HU, altos de HU e com textura grosseira Após RT, os descritores radiómicos associados à PET indicam nos doentes sem resposta à QT e na ausência de taxas de sedimentação ≥50mm/h sem sintomas B maior homogeneidade e na modalidade CT nos doentes com resposta à QT maior homogeneidade. Mesmo sem robustez estatística pode-se verificar potencial prognóstico de resposta aos tratamentos das características GLRLM_GLNU, na modalidade PET, e NGLDM_Coarseness, na modalidade CT, que tiveram evolução em todas as fases dos tratamentos. Em geral este trabalho permitiu, de forma exploratória, evidenciar o potencial de prognóstico dos descritores radiómicos multimodais no suporte à terapêutica dos Linfomas de Hodgkin.The present work proposes to explore the value and the concept of radiomics in a retrospective study of PET/CT/RT in study of pre chemotherapy QT), post QT and post RT in Hodgkin’s Lymphomas with 17 patients. The work begins by overviewing the state of the art about Hodgkin Lymphomas (from its diagnosis and staging to treatment and expected therapeutic results), as well as the use of PET/CT (physical principles and their importance in lymphomas) and finally the radiomics workflow (acquisition, segmentation, characteristics, clinical utility, trends, challenges and limitations). Then it exposes the materials and methods used for execution of radiomics pipeline, ending with the description and discussion of the obtained results with the data analysis and statistics with clinic drivers. The radiomic process described here is clearly determined by the specific context of radiotherapy. We found that the obtained quantitative results are compatible with the clinical results, being all the process ready to be scalable to other patients. Despite the small size of the database, we guarantee dataset consistency due to the strict fulfillment to clinical and image acquisition protocols. Regarding the textural radiomic descriptors, there is greater homogeneity after QT in the PET modality in patients without bulky disease and who responded to QT and in the patients without symptoms B there are few large areas with high SUV values. Patients with three or more involved ganglion areas present grater uniformity and homogeneity, being higher in females. In CT modality, there is a greater homogeneity in patients with QT response and in patients without three or more ganglion areas involved decrease of large size zones with low HU values, high HU values and coarse texture. After RT, PET associated radiomic descriptors indicate in patients without QT response and in the absence of sedimentation rates ≥50 mm/h without symptoms B greater homogeneity and in CT modality in patients with QT response greater homogeneity. Even without statistical robustness, it is possible to verify the potential prognosis of the characteristics GLRLM_GLNU, in PET modality, and NGLDM_coarseness, in the CT modality, which had evolution in all treatment phases. In general, in this work, we assessed, in an exploratory way, the prognostic potential of multimodal radiomic descriptors within the context of Hodgkin’s Lymphoma therapy.Mestrado em Tecnologias da Imagem Médic