Identification of Individual Glandular Regions Using LCWT and Machine Learning Techniques

A new approach for the segmentation of gland units in histological images is proposed with the aim of contributing to the improvement of the prostate cancer diagnosis. Clustering methods on several colour spaces are applied to each sample in order to generate a binary mask of the different tissue components. From the mask of lumen candidates, the Locally Constrained Watershed Transform (LCWT) is applied as a novel gland segmentation technique never before used in this type of images. 500 random gland candidates, both benign and pathological, are selected to evaluate the LCWT technique providing results of Dice coefficient of 0.85. Several shape and textural descriptors in combination with contextual features and a fractal analysis are applied, in a novel way, on different colour spaces achieving a total of 297 features to discern between artefacts and true glands. The most relevant features are then selected by an exhaustive statistical analysis in terms of independence between variables and dependence with the class. 3.200 artefacts, 3.195 benign glands and 3.000 pathological glands are obtained, from a data set of 1468 images at 10x magnification. A careful strategy of data partition is implemented to robustly address the classification problem between artefacts and glands. Both linear and non-linear approaches are considered using machine learning techniques based on Support Vector Machines (SVM) and feedforward neural networks achieving values of sensitivity, specificity and accuracy of 0.92, 0.97 and 0.95, respectivelyThis work has been funded by the Ministry of Economy, Industry and Competitiveness under the SICAP project (DPI2016-77869-C2-1-R). The work of Adri´an Colomer has been supported by the Spanish FPI Grant BES-2014-067889. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this researchGarcía-Pardo, JG.; Colomer, A.; Naranjo Ornedo, V.; Peñaranda, F.; Sales, MÁ. (2018). Identification of Individual Glandular Regions Using LCWT and Machine Learning Techniques. En Intelligent Data Engineering and Automated Learning – IDEAL 2018. Springer. 642-650. https://doi.org/10.1007/978-3-030-03493-1_67S642650Gleason, D.F.: Histologic grading and clinical staging of prostatic carcinoma. In: Urologic Pathology (1977)Naik, S., Doyle, S., Feldman, M., Tomaszewski, J., Madabhushi, A.: Gland segmentation and computerized gleason grading of prostate histology by integrating low-, high-level and domain specific information. In: MIAAB Workshop, pp. 1–8 (2007)Nguyen, K., Sabata, B., Jain, A.K.: Prostate cancer grading: gland segmentation and structural features. Pattern Recogn. Lett. 33(7), 951–961 (2012)Kwak, J.T., Hewitt, S.M.: Multiview boosting digital pathology analysis of prostate cancer. Comput. Methods Programs Biomed. 142, 91–99 (2017)Ren, J., Sadimin, E., Foran, D.J., Qi, X.: Computer aided analysis of prostate histopathology images to support a refined gleason grading system. In: SPIE Medical Imaging, International Society for Optics and Photonics, p. 101331V (2017)Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Berlin (2013)Nguyen, K., Sarkar, A., Jain, A.K.: Structure and context in prostatic gland segmentation and classification. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 115–123. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33415-3_15Beare, R.: A locally constrained watershed transform. IEEE Trans. Pattern Anal. Mach. Intell. 28(7), 1063–1074 (2006)Gertych, A., et al.: Machine learning approaches to analyze histological images of tissues from radical prostatectomies. Comput. Med. Imaging Graph. 46, 197–208 (2015)Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)Huang, P., Lee, C.: Automatic classification for pathological prostate images based on fractal analysis. IEEE Trans. Med. Imaging 28(7), 1037–1050 (2009)Ruifrok, A.C., Johnston, D.A., et al.: Quantification of histochemical staining by color deconvolution. Anal. Quant. Cytol. Histol. 23(4), 291–299 (2001

Automatic detection of malignant prostatic gland units in cross-sectional microscopic images

Prostate cancer is the second most frequent cause of cancer deaths among men in the US. In the most reliable screening method, histological images from a biopsy are examined under a microscope by pathologists. In an early stage of prostate cancer, only relatively few gland units in a large region become malignant. Discovering such sparse malignant gland units using a microscope is a labor-intensive and error-prone task for pathologists. In this paper, we develop effective image segmentation and classification methods for automatic detection of malignant gland units in microscopic images. Both segmentation and classification methods are based on carefully designed feature descriptors, including color histograms and texton co-occurrence tables. © 2010 IEEE.published_or_final_versionThe 17th IEEE International Conference on Image Processing (ICIP 2010), Hong Kong, China, 26-29 September 2010. In Proceedings of the 17th ICIP, 2010, p. 1057-106

Image analysis discloses differences in nuclear parameters between ERG+ and ERG- prostatic carcinomas

Prostatic carcinoma (PC) is the most frequent urologic cancer and one of the most frequent cancers in males; it is a heterogeneous disease, in terms of molecular features, morphology and prognosis. About half of cases depends on TMPRSS2-ETS translocation which leads to a production of ERG transcription factor. ERG+ and ERG– cancers seem to differ in a number of features, which could lead to an altered nuclear structure; the aim of the study was to test this hypothesis. The material consisted of total 39 PC cases, representing ERG+ and ERG–, as well as Gleason pattern 3 and 4. Filtering by color deconvolution and automatic segmentation were used, and the properly detected nuclei were manually selected. From each case fifty nuclei were obtained; then geometric features and texture parameters were assessed. The analysis of the collected data showed differences both between ERG+/ERG– and Gleason pattern 3 and 4 cases in most of the features analyzed. Our results suggest that indeed the ERG status, thus likely TMPRSS2-ETS translocation, has an impact on morphology of nuclei in PC, and their differences are evident enough to be detectable by image analysis

Colorectal Cancer Tissue Classification Based on Machine Learning

For digital pathology, automatic recognition of different tissue types in histological images is important for diagnostic assistance and healthcare. Since histological images generally contain more than one tissue type, multi-class texture analysis plays a critical role to solve this problem. This study examines the important statistical features including Gray Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), Spatial filters, Wiener filter, Gabor filters, Haralick features, fractal filters, and local binary pattern (LBP) for colorectal cancer tissue identification by using support vector machine (SVM) and decision fusion of feature selection. The average experimental results achieve high identification rate which is significantly superior to the existing known methods. In summary, the proposed method based on machine learning outperforms the techniques described in the literatures and achieve high classification accuracy rate at 93.17% for eight classes and 96.02% for ten classes which demonstrate promising applications for cancer tissue classification of histological image

Improving cancer subtype diagnosis and grading using clinical decision support system based on computer-aided tissue image analysis

This research focuses towards the development of a clinical decision support system (CDSS) based on cellular and tissue image analysis and classification system that improves consistency and facilitates the clinical decision making process. In a typical cancer examination, pathologists make diagnosis by manually reading morphological features in patient biopsy images, in which cancer biomarkers are highlighted by using different staining techniques. This process is subjected to pathologist's training and experience, especially when the same cancer has several subtypes (i.e. benign tumor subtype vs. malignant subtype) and the same cancer tissue biopsy contains heterogeneous morphologies in different locations. The variability in pathologist's manual reading may result in varying cancer diagnosis and treatment. This Ph.D. research aims to reduce the subjectivity and variation existing in traditional histo-pathological reading of patient tissue biopsy slides through Computer-Aided Diagnosis (CAD). Using the CAD, quantitative molecular profiling of cancer biomarkers of stained biopsy images are obtained by extracting and analyzing texture and cellular structure features. In addition, cancer sub-type classification and a semi-automatic grade scoring (i.e. clinical decision making) for improved consistency over a large number of cancer subtype images can be performed. The CAD tools do have their own limitations and in certain cases the clinicians, however, prefer systems which are flexible and take into account their individuality when necessary by providing some control rather than fully automated system. Therefore, to be able to introduce CDSS in health care, we need to understand users' perspectives and preferences on the new information technology. This forms as the basis for this research where we target to present the quantitative information acquired through the image analysis, annotate the images and provide suitable visualization which can facilitate the process of decision making in a clinical setting.PhDCommittee Chair: Dr. May D. Wang; Committee Member: Dr. Andrew N. Young; Committee Member: Dr. Anthony J. Yezzi; Committee Member: Dr. Edward J. Coyle; Committee Member: Dr. Paul Benkese

Machine learning strategies for diagnostic imaging support on histopathology and optical coherence tomography

Tesis por compendio[ES] Esta tesis presenta soluciones de vanguardia basadas en algoritmos de computer vision (CV) y machine learning (ML) para ayudar a los expertos en el diagnóstico clínico. Se centra en dos áreas relevantes en el campo de la imagen médica: la patología digital y la oftalmología. Este trabajo propone diferentes paradigmas de machine learning y deep learning para abordar diversos escenarios de supervisión en el estudio del cáncer de próstata, el cáncer de vejiga y el glaucoma. En particular, se consideran métodos supervisados convencionales para segmentar y clasificar estructuras específicas de la próstata en imágenes histológicas digitalizadas. Para el reconocimiento de patrones específicos de la vejiga, se llevan a cabo enfoques totalmente no supervisados basados en técnicas de deep-clustering. Con respecto a la detección del glaucoma, se aplican algoritmos de memoria a corto plazo (LSTMs) que permiten llevar a cabo un aprendizaje recurrente a partir de volúmenes de tomografía por coherencia óptica en el dominio espectral (SD-OCT). Finalmente, se propone el uso de redes neuronales prototípicas (PNN) en un marco de few-shot learning para determinar el nivel de gravedad del glaucoma a partir de imágenes OCT circumpapilares. Los métodos de inteligencia artificial (IA) que se detallan en esta tesis proporcionan una valiosa herramienta de ayuda al diagnóstico por imagen, ya sea para el diagnóstico histológico del cáncer de próstata y vejiga o para la evaluación del glaucoma a partir de datos de OCT.[CA] Aquesta tesi presenta solucions d'avantguarda basades en algorismes de *computer *vision (CV) i *machine *learning (ML) per a ajudar als experts en el diagnòstic clínic. Se centra en dues àrees rellevants en el camp de la imatge mèdica: la patologia digital i l'oftalmologia. Aquest treball proposa diferents paradigmes de *machine *learning i *deep *learning per a abordar diversos escenaris de supervisió en l'estudi del càncer de pròstata, el càncer de bufeta i el glaucoma. En particular, es consideren mètodes supervisats convencionals per a segmentar i classificar estructures específiques de la pròstata en imatges histològiques digitalitzades. Per al reconeixement de patrons específics de la bufeta, es duen a terme enfocaments totalment no supervisats basats en tècniques de *deep-*clustering. Respecte a la detecció del glaucoma, s'apliquen algorismes de memòria a curt termini (*LSTMs) que permeten dur a terme un aprenentatge recurrent a partir de volums de tomografia per coherència òptica en el domini espectral (SD-*OCT). Finalment, es proposa l'ús de xarxes neuronals *prototípicas (*PNN) en un marc de *few-*shot *learning per a determinar el nivell de gravetat del glaucoma a partir d'imatges *OCT *circumpapilares. Els mètodes d'intel·ligència artificial (*IA) que es detallen en aquesta tesi proporcionen una valuosa eina d'ajuda al diagnòstic per imatge, ja siga per al diagnòstic histològic del càncer de pròstata i bufeta o per a l'avaluació del glaucoma a partir de dades d'OCT.[EN] This thesis presents cutting-edge solutions based on computer vision (CV) and machine learning (ML) algorithms to assist experts in clinical diagnosis. It focuses on two relevant areas at the forefront of medical imaging: digital pathology and ophthalmology. This work proposes different machine learning and deep learning paradigms to address various supervisory scenarios in the study of prostate cancer, bladder cancer and glaucoma. In particular, conventional supervised methods are considered for segmenting and classifying prostate-specific structures in digitised histological images. For bladder-specific pattern recognition, fully unsupervised approaches based on deep-clustering techniques are carried out. Regarding glaucoma detection, long-short term memory algorithms (LSTMs) are applied to perform recurrent learning from spectral-domain optical coherence tomography (SD-OCT) volumes. Finally, the use of prototypical neural networks (PNNs) in a few-shot learning framework is proposed to determine the severity level of glaucoma from circumpapillary OCT images. The artificial intelligence (AI) methods detailed in this thesis provide a valuable tool to aid diagnostic imaging, whether for the histological diagnosis of prostate and bladder cancer or glaucoma assessment from OCT data.García Pardo, JG. (2022). Machine learning strategies for diagnostic imaging support on histopathology and optical coherence tomography [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/182400Compendi

Ensemble Learning of Tissue Components for Prostate Histopathology Image Grading

Ensemble learning is an effective machine learning approach to improve the prediction performance by fusing several single classifier models. In computer-aided diagnosis system (CAD), machine learning has become one of the dominant solutions for tissue images diagnosis and grading. One problem in a single classifier model for multi-components of the tissue images combination to construct dense feature vectors is the overfitting. In this paper, an ensemble learning for multi-component tissue images classification approach is proposed. The prostate cancer Hematoxylin and Eosin (H&E) histopathology images from HUKM were used to test the proposed ensemble approach for diagnosing and Gleason grading. The experiments results of several prostate classification tasks, namely, benign vs. Grade 3, benign vs.Grade4, and Grade 3vs.Grade 4 show that the proposed ensemble significantly outperforms the previous typical CAD and the naïve approach that combines the texture features of all tissue component directly in dense feature vectors for a classifier

Medición sobre MRI para diagnóstico de cáncer de próstata

The male reproductive system has a gland located below the bladder and in front of the rectum: the prostate. It surrounds the urethra and has the function of producing a fluid component in the seminal fluid. Over time, this gland tends to enlarge and block the urethra, making it difficult to urinate or sexual function. This alteration is known as harmless prostatic hyperplasia, which is corrected with surgery. Sometimes it is confused with prostate cancer due to the similarity of the symptoms, which is frequent in men. Diagnosis of this disease is generally made using a manual technique called a digital rectal examination and a laboratory test that measures PSA levels in the blood. It is a substance found in the blood of someone who usually has prostate cancer. Additionally, the diagnosis is supported by a transrectal ultrasound through a catheter. This comprehensive process helps to determine the extension of prostate cancer and designate the correct treatment. The status of prostate injury is assessed by practicing a Magnetic Resonance Imaging (MRI). It is a procedure performed by radio waves and a computer that creates detailed prostate areas' images. It analyzes the prostate condition and determines the procedure or treatment according to the injury's status, for example, surgery, radiation therapy, or monitored observation. To define what kind of treatment, it is essential to analyze the different disease stages and the Gleason Score, a measurement of the histological grade, ranging from 2 to 10, that indicates the probability of spreading or extending the tumor. This research focuses on the analysis and the extraction of measurements to classify forms of prostate lesions to support its diagnosis. It considers the PI-RADS categorization, which currently determines the probability of suffering from clinically significant prostate cancer. For this purpose, an analysis was made using a geometric interpretation from different categorizations of cancer (4-5). A digital processing of Python images on T2, ADC, and DWI was made applicating the concept of the curve, Zernike moments, fractal dimension, Caliper dimension, the total absolute curvature, the energy bending, direction, convexity, circularity, compactness, Hu moments, dimension, eccentricity, extent, solidity, orientation, largest axis length, smallest axis length, radius, center, centroid, length, area.El aparato reproductor masculino tiene una glándula ubicada debajo de la vejiga y frente al recto: la próstata. Rodea la uretra y tiene la función de producir un componente líquido en el líquido seminal. Con el tiempo, esta glándula tiende a agrandarse y bloquear la uretra, lo que dificulta la micción o la función sexual. Esta alteración se conoce como hiperplasia prostática, que se corrige con cirugía. En ocasiones se confunde con el cáncer de próstata por la similitud de los síntomas, que es frecuente en los hombres. El diagnóstico de esta enfermedad generalmente se realiza mediante una técnica manual llamada tacto rectal y una prueba de laboratorio que mide los niveles de PSA en la sangre. Es una sustancia que se encuentra en la sangre de una persona que suele tener cáncer de próstata. Además, el diagnóstico se apoya en una ecografía transrectal a través de un catéter. Este proceso integral ayuda a determinar la extensión del cáncer de próstata y a designar el tratamiento correcto. El estado de la lesión de próstata se evalúa mediante la práctica de una resonancia magnética (MRI). Es un procedimiento realizado por ondas de radio y una computadora que crea imágenes detalladas de áreas de la próstata. Analiza la condición de la próstata y determina el procedimiento o tratamiento de acuerdo con el estado de la lesión, por ejemplo, cirugía, radioterapia u observación monitoreada. Para definir qué tipo de tratamiento es fundamental analizar los diferentes estadios de la enfermedad y el Gleason Score, una medida del grado histológico, que va de 2 a 10, que indica la probabilidad de diseminación o extensión del tumor. Esta investigación se centra en el análisis y la extracción de medidas para clasificar formas de lesiones prostáticas que apoyen su diagnóstico. Considera la categorización PI-RADS, que actualmente determina la probabilidad de padecer cáncer de próstata clínicamente significativo. Para ello, se realizó un análisis utilizando una interpretación geométrica de diferentes categorizaciones de cáncer (4-5). Se realizó un procesamiento digital de imágenes de Python en T2, ADC y DWI aplicando el concepto de curva, momentos Zernike, dimensión fractal, dimensión Caliper, la curvatura absoluta total, la flexión de energía, dirección, convexidad, circularidad, compacidad, momentos Hu, dimensión, excentricidad, extensión, solidez, orientación, longitud del eje más grande, longitud del eje más pequeño, radio, centro, centroide, longitud y área