Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Histopathological image analysis: a review,”

Abstract-Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Ανάπτυξη Συστήματος Αυτόματης Ταξινόμησης Όγκων Εγκεφάλου σε Πλατφόρμα Τηλεπαθολογίας με Βάση Ιστοπαθολογικές Εικόνες

Στα πλαίσια της παρούσας εργασίας αναπτύχθηκε σύστημα τηλεπαθολογίας και υποβοήθησης της διάγνωσης για την βελτίωση της ακρίβειας ταξινόμησης καρκίνων εγκεφάλου σε βαθμούς κακοήθειας. Το κλινικό υλικό που χρησιμοποιήθηκε ήταν δείγματα ιστοπαθολογικού ιστού, που συλλέχτηκαν από 35 ασθενείς από το Πανεπιστημιακό Νοσοκομείο της Πάτρας, οι οποίοι είχαν διαγνωστεί με αστροκυτταρικό όγκο. Πραγματοποιήθηκε προετοιμασία των δεδομένων κατά την οποία τα δείγματα ιστού χρωματίστηκαν με H&E (Hematoxylin & Eosin), ώστε να φανερωθούν τα διάφορα συστατικά των κυττάρων και των πυρήνων αλλά και για να διακριθούν μεταξύ τους. Στη συνέχεια, ένας ιστοπαθολόγος εξέτασε τις χρωματισμένες εικόνες των δειγμάτων ιστού και πραγματοποίησε διάγνωση με βάση ιστολογικά κριτήρια σε τρεις βαθμούς κακοήθειας (Ι, ΙΙ, ΙΙΙ ή ΙV) σύμφωνα με τον Παγκόσμιο Οργανισμό Υγείας (World Health Organization - WHO). Ταυτόχρονα, ο ίδιος ιστοπαθολόγος σημείωσε πάνω στις πλάκες των δειγμάτων την πιο αντιπροσωπευτική περιοχή (Region Of Interest - ROI) για την εξέταση αυτών. Έτσι, ακολούθησε η ψηφιοποίηση της εικόνας, όπου από κάθε δείγμα ψηφιοποιήθηκαν εικόνες, η οποίες πάρθηκαν από το προκαθορισμένο ROI και εφαρμόστηκε τμηματοποίηση πυρήνων. Το σημαντικότερο στοιχείο μιας τέτοιας εικόνας, από το οποίο μπορούν να εξαχθούν συμπεράσματα για την διάγνωση, είναι οι πυρήνες. Επομένως, δημιουργήθηκαν αλγόριθμοι, οι οποίοι απομάκρυναν όλες τις υπόλοιπες περιοχές της εικόνας και τμηματοποίησαν τους πυρήνες για περισσότερη ανάλυση αυτών. Από τα αποτελέσματα της τμηματοποίησης των εικόνων προέκυψε ότι για τον υπολογισμό των χαρακτηριστικών της παρούσας εργασίας απομονώθηκαν και μελετήθηκαν συνολικά κατά μέσο όρο 588 τμηματοποιημένοι πυρήνες για κάθε δείγμα- ασθενή, ενόσω έχει αποδειχθεί ότι ακόμη και 200 ορθά τμηματοποιημένοι πυρήνες είναι επαρκείς για την εξαγωγή χαρακτηριστικών. Κατόπιν, εξήχθησαν χαρακτηριστικά μορφολογίας, υφής και αρχιτεκτονικής από τους τμηματοποιημένους πυρήνες για να περιγράψουν τον βαθμό κακοήθειας του κάθε δείγματος-ασθενή. Τα χαρακτηριστικά αυτά αποτέλεσαν την είσοδο σε ένα σύστημα αναγνώρισης προτύπων που σχεδιάστηκε, έτσι ώστε να προβλέπει την επικινδυνότητα του κάθε όγκου. Το σύστημα αυτό δομείται με αλγορίθμους supervised, semi-supervised και unsupervised. Ο SVM Supervised Classif ier (Polynomial ή Quadratic kernel) αποτέλεσε μια λύση στην αυτοματοποιημένη ταξινόμηση αστροκυττωμάτων, καθώς έδωσε ποσοστό ακρίβειας 94.29% στην ταξινόμηση των δεδομένων. Oι unsupervised clustering k-Means και Fuzzy έδωσαν ποσοστό ακρίβειας 74.29% και ο Semi-Supervised with Co-Training ταξινομητής έδωσε μέγιστο ποσοστό ακρίβειας 88.57%. Τέλος, τα χαρακτηριστικά Mean (χαρακτηριστικό υφής 1ης τάξης), mean Correlation, range Correlation, mean Gray Level Non-Uniformity και mean Run Length Non-Uniformity (χαρακτηριστικά υφής 2ης τάξης) αναδείχθηκαν τα χαρακτηριστικά εκείνα με το μεγαλύτερο ποσοστό πιθανότητας εμφάνισης στο διάνυσμα χαρακτηριστικών εκείνο, το οποίο δίνει το μέγιστο (καλύτερο) ποσοστό ακρίβειας ενός ταξινομητή.In this thesis, a telepathology system has been developed which assists in improving diagnostic accuracy of brain cancer classification into grades of malignancy. The clinical material was histopathological tissue samples, which were collected from 35 patients from the University Hospital of Patra, who were diagnosed with astrocytic tumor. During data preparation the tissue samples were stained with H & E (Hematoxylin & Eosin), in order to expose the various components of the cells and nuclei. Then, a histopathologist examined the stained histopathological images of tissue samples and performed diagnosis based on histological criteria of malignancy in three grades (I, II, III or IV) according to the World Health Organization (WHO). At the same time, the histopathologist noted on the samples the most representative region (Region of Interest - ROI) for the examination. Thus, each sample image was digitized, taken from the predefined ROI and applied nuclei segmentation. The most important element of such a picture, from which conclusions can be drawn for the diagnosis, is the nucleus. Therefore, algorithms were created, which removed all remaining areas of the image and segmented nuclei for further analysis of these. From the results of image segmentation revealed that overall average 588 segmented nuclei were isolated and studied for each patient sample, while it has been shown that even 200 correctly segmented nuclei are sufficient to feature extraction. Then morphological characteristics, texture and architecture of their segmented nuclei were exported to describe the degree of malignancy of each sample-patient relationship. These characteristics were entering a pattern recognition system designed to provide the dangerousness of each tumor. This system was constructed by supervised, semi-supervised and unsupervised algorithms. The SVM Supervised Classifier (Polynomial or Quadratic kernel) has been proved a solution in the automated classification of astrocytomas as it had an accuracy rate of 94.29% in classifying data. The unsupervised clustering k-Means and Fuzzy gave an accuracy rate of 74.29% and Semi-Supervised with Co-Training classifier gave maximum accuracy rate of 88.57%. Finally, the characteristics Mean (first class texture feature), mean Correlation, range Correlation, mean Gray Level Non-Uniformity and mean Run Length Non-Uniformity (second class textural) highlighted as those features with the highest percentage likelihood to appear in feature vector that gives the maximum (best) accuracy rate of a classifier

An automated classification system to determine malignant grades of brain tumour (glioma) in magnetic resonance images based on meta-trainable multiple classifier schemes

The accurate classification of malignant grades of brain tumours is crucial for therapeutic planning as it impacts on the tumour’s prognosis, where the higher the malignancy levels of the brain tumour are, the higher the mortality rate is. It is also essential to provide patients with appropriate clinical management that may prolong survival and improve their quality of life. Determining the malignant grade of a brain tumour is a critical challenge because different malignant grades of brain tumours, in some cases, have inconsistent and mixed morphological characteristics. Consequently, the visual diagnosis using only the naked eye is a very complex and challenging task. The most common type of brain tumour is glioma. According to the World Health Organisation, low-grade glioma, which includes grade I and grade II are the least malignant, slow growing, and respond well to treatment. While, high-grade gliomas, which include grade III and grade IV are extremely malignant, have a poor prognosis and may lead to a high mortality rate. Hence, the motivation to develop an automated classification system to predict the malignant grade of glioma is the aim of this research. To achieve this aim, several novel methods were developed and this includes new methods for the extraction of statistical measures, selection of the dominant predictors, and the fusion of multi-classification models. The integration of these stages generates an accurate and automated decision system to determine the malignant grade of glioma. The feature extraction starts from the viewpoint that the objective measure of the brain tumour descriptors in MR images lead to an accurate classification of malignant brain tumours. This work starts from the standpoint that meta-trainable fusion of multiple classifier models can offer a better classification accuracy to recognise the malignant grade of glioma in MR images. This study developed a novel strategy based on two stages of multiple classifier systems for glioma grades. In the first stage, different machine learning algorithms were used. In the second stage, a systematic trainable combiner was designed based on deep neural networks. This research was validated using four benchmark datasets of MR images, which are publicly available and confirmed with the histopathological diagnosis. The proposed system was also evaluated and compared against different traditional algorithms; the experimental results showed that the proposed system has successfully achieved better and optimal discrimination in glioma grades on all dataset

Tumour grading and discrimination based on class assignment and quantitative texture analysis techniques

Medical imaging represents the utilisation of technology in biology for the purpose of noninvasively revealing the internal structure of the organs of the human body. It is a way to improve the quality of the patient's life through a more precise and rapid diagnosis, and with limited side-effects, leading to an effective overall treatment procedure. The main objective of this thesis is to propose novel tumour discrimination techniques that cover both micro and macro-scale textures encountered in computed tomography (CI') and digital microscopy (DM) modalities, respectively. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and classification. The fractal dimension (FO) as a texture measure was applied to contrast enhanced CT lung tumour images in an aim to improve tumour grading accuracy from conventional CI' modality, and quantitative performance analysis showed an accuracy of 83.30% in distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant tumours. A different approach was adopted for subtype discrimination of brain tumour OM images via a set of statistical and model-based texture analysis algorithms. The combined Gaussian Markov random field and run-length matrix texture measures outperformed all other combinations, achieving an overall class assignment classification accuracy of 92.50%. Also two new histopathological multi resolution approaches based on applying the FO as the best bases selection for discrete wavelet packet transform, and when fused with the Gabor filters' energy output improved the accuracy to 91.25% and 95.00%, respectively. While noise is quite common in all medical imaging modalities, the impact of noise on the applied texture measures was assessed as well. The developed lung and brain texture analysis techniques can improve the physician's ability to detect and analyse pathologies leading for a more reliable diagnosis and treatment of disease

Brain Tumor Growth Modelling .

Prediction methods of Glioblastoma tumors growth constitute a hard task due to the lack of medical data, which is mostly related to the patients’ privacy, the cost of collecting a large medical dataset, and the availability of related notations by experts. In this thesis, we study and propose a Synthetic Medical Image Generator (SMIG) with the purpose of generating synthetic data based on Generative Adversarial Network in order to provide anonymized data. In addition, to predict the Glioblastoma multiform (GBM) tumor growth we developed a Tumor Growth Predictor (TGP) based on End to End Convolution Neural Network architecture that allows training on a public dataset from The Cancer Imaging Archive (TCIA), combined with the generated synthetic data. We also highlighted the impact of implicating a synthetic data generated using SMIG as a data augmentation tool. Despite small data size provided by TCIA dataset, the obtained results demonstrate valuable tumor growth prediction accurac

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide 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 toward appropriate cancer care. Indeed, the need to apply 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.