9 research outputs found
Geodesic tractography segmentation for directional medical image analysis
Acknowledgements page removed per author's request, 01/06/2014.Geodesic Tractography Segmentation is the two component approach presented in this thesis for the analysis of imagery in oriented domains, with emphasis on the application to diffusion-weighted magnetic resonance imagery (DW-MRI). The computeraided analysis of DW-MRI data presents a new set of problems and opportunities for the application of mathematical and computer vision techniques. The goal is to develop a set of tools that enable clinicians to better understand DW-MRI data and ultimately shed new light on biological processes.
This thesis presents a few techniques and tools which may be used to automatically find and segment major neural fiber bundles from DW-MRI data. For each technique, we provide a brief overview of the advantages and limitations of our approach relative to other available approaches.Ph.D.Committee Chair: Tannenbaum, Allen; Committee Member: Barnes, Christopher F.; Committee Member: Niethammer, Marc; Committee Member: Shamma, Jeff; Committee Member: Vela, Patrici
ΠΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΡΠΉ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡ ΠΏΡΠΈ ΠΊΠΎΠ»ΠΎΡΠ΅ΠΊΡΠ°Π»ΡΠ½ΠΎΠΌ ΡΠ°ΠΊΠ΅: ΠΎΠ±Π·ΠΎΡ
The study objective: the study objective is to examine the use of artificial intelligence (AI) in the diagnosis, treatment, and prognosis of Colorectal Cancer (CRC) and discuss the future potential of AI in CRC. Material and Methods. The Web of Science, Scopus, PubMed, Medline, and eLIBRARY databases were used to search for the publications. A study on the application of Artificial Intelligence (AI) to the diagnosis, treatment, and prognosis of Colorectal Cancer (CRC) was discovered in more than 100 sources. In the review, data from 83 articles were incorporated. Results. The review article explores the use of artificial intelligence (AI) in medicine, specifically focusing on its applications in colorectal cancer (CRC). It discusses the stages of AI development for CRC, including molecular understanding, image-based diagnosis, drug design, and individualized treatment. The benefits of AI in medical image analysis are highlighted, improving diagnosis accuracy and inspection quality. Challenges in AI development are addressed, such as data standardization and the interpretability of machine learning algorithms. The potential of AI in treatment decision support, precision medicine, and prognosis prediction is discussed, emphasizing the role of AI in selecting optimal treatments and improving surgical precision. Ethical and regulatory considerations in integrating AI are mentioned, including patient trust, data security, and liability in AI-assisted surgeries. The review emphasizes the importance of an AI standard system, dataset standardization, and integrating clinical knowledge into AI algorithms. Overall, the article provides an overview of the current research on AI in CRC diagnosis, treatment, and prognosis, discussing its benefits, challenges, and future prospects in improving medical outcomes.Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ - ΠΎΡΠ΅Π½ΠΊΠ° Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠ΅ΠΉ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΠ° (ΠΠ) Π² Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅, Π»Π΅ΡΠ΅Π½ΠΈΠΈ ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΊΠΎΠ»ΠΎΡΠ΅ΠΊΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΠΊΠ° (ΠΠ Π ), Π° ΡΠ°ΠΊΠΆΠ΅ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Π° ΠΠ Π² Π»Π΅ΡΠ΅Π½ΠΈΠΈ ΠΠ Π . ΠΠ°ΡΠ΅ΡΠΈΠ°Π» ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ ΠΏΠΎΠΈΡΠΊ Π½Π°ΡΡΠ½ΡΡ
ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ Π² ΠΏΠΎΠΈΡΠΊΠΎΠ²ΡΡ
ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
Web of Science, Scopus, PubMed, Medline ΠΈ eLIBRARY. ΠΡΠ»ΠΎ ΠΏΡΠΎΡΠΌΠΎΡΡΠ΅Π½ΠΎ Π±ΠΎΠ»Π΅Π΅ 100 ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ² ΠΏΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΠ Π΄Π»Ρ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ, Π»Π΅ΡΠ΅Π½ΠΈΡ ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΠ Π . Π ΠΎΠ±Π·ΠΎΡ Π²ΠΊΠ»ΡΡΠ΅Π½Ρ Π΄Π°Π½Π½ΡΠ΅ ΠΈΠ· 83 ΡΡΠ°ΡΠ΅ΠΉ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ Π°Π½Π°Π»ΠΈΠ· Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΡ, ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π½ΠΎΠΉ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΠ° Π² ΠΌΠ΅Π΄ΠΈΡΠΈΠ½Π΅, ΠΎΡΠΎΠ±ΠΎΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ ΡΠ΄Π΅Π»Π΅Π½ΠΎ Π΅Π³ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΈ ΠΊΠΎΠ»ΠΎΡΠ΅ΠΊΡΠ°Π»ΡΠ½ΠΎΠΌ ΡΠ°ΠΊΠ΅. ΠΠ±ΡΡΠΆΠ΄Π°ΡΡΡΡ ΡΡΠ°ΠΏΡ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΠ ΠΏΡΠΈ ΠΠ Π , Π²ΠΊΠ»ΡΡΠ°Ρ ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΡΡ Π²Π΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ, Π»ΡΡΠ΅Π²ΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΡ, ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΡ Π»Π΅ΠΊΠ°ΡΡΡΠ² ΠΈ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΠΎΠ΅ Π»Π΅ΡΠ΅Π½ΠΈΠ΅. ΠΠΎΠ΄ΡΠ΅ΡΠΊΠ½ΡΡΡ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π° ΠΠ Π² Π°Π½Π°Π»ΠΈΠ·Π΅ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, ΡΠ°ΠΊΠΈΡ
ΠΊΠ°ΠΊ ΠΠ’, ΠΠ Π’ ΠΈ ΠΠΠ’, ΡΡΠΎ ΠΏΠΎΠ²ΡΡΠ°Π΅Ρ ΡΠΎΡΠ½ΠΎΡΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ. Π Π°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΡΡ ΡΠ°ΠΊΠΈΠ΅ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΠ, ΠΊΠ°ΠΊ ΡΡΠ°Π½Π΄Π°ΡΡΠΈΠ·Π°ΡΠΈΡ Π΄Π°Π½Π½ΡΡ
ΠΈ ΠΈΠ½ΡΠ΅ΡΠΏΡΠ΅ΡΠΈΡΡΠ΅ΠΌΠΎΡΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ. ΠΠΎΠ΄ΡΠ΅ΡΠΊΠΈΠ²Π°Π΅ΡΡΡ ΡΠΎΠ»Ρ ΠΠ Π² Π²ΡΠ±ΠΎΡΠ΅ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ ΡΠ°ΠΊΡΠΈΠΊΠΈ Π»Π΅ΡΠ΅Π½ΠΈΡ ΠΈ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠΈ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Ρ
ΠΈΡΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π²ΠΌΠ΅ΡΠ°ΡΠ΅Π»ΡΡΡΠ²Π°. Π£ΡΠΈΡΡΠ²Π°ΡΡΡΡ ΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈ Π½ΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΡΠ΅ Π°ΡΠΏΠ΅ΠΊΡΡ ΠΠ, Π²ΠΊΠ»ΡΡΠ°Ρ Π΄ΠΎΠ²Π΅ΡΠΈΠ΅ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ², Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΡ Π΄Π°Π½Π½ΡΡ
ΠΈ ΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎΡΡΡ Π² ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠΈ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΠ. ΠΠ±ΡΡΠΆΠ΄Π°ΡΡΡΡ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π° ΠΠ Π² Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅, Π»Π΅ΡΠ΅Π½ΠΈΠΈ ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΊΠΎΠ»ΠΎΡΠ΅ΠΊΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΠΊΠ°, ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΠΈ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Ρ ΡΠ»ΡΡΡΠ΅Π½ΠΈΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² Π»Π΅ΡΠ΅Π½ΠΈΡ
Segmentation and polyp detection in virtual colonoscopy : a complete system for computer aided diagnosis
El cancer colorectal es una de las mayores causas de muerte por cancer en el mundo. La deteccion temprana de polipos es fundamental para su tratamiento, permitiendo alcanzar tasas del 90% de curabilidad. La tecnica habitual para la deteccion de polipos, debido a su elevada performance, es la colonoscopia optica (tecnica invasiva y extremadamente cara). A mediados de los '90 surge la tecnica denominada colonoscopia virtual. Esta tecnica consiste en la reconstruccion 3D del colon a partir de cortes de tomografia computada. Es por ende una tecnica no invasiva, y relativamente barata, pero la cantidad de falsos positivos y falsos negativos producida por estos metodos esta muy por encima de los maximos aceptados en la practica medica. Los avances recientes en las tecnicas de imagenologia parecerian hacer posible la reduccion de estas tasas. Como consecuencia de esto, estamos asistiendo a un nuevo interes por la colonoscopia virtual. En este trabajo se presenta un sistema completo de diagnostico asistido por computadora. La primera etapa del sistema es la segmentacion, que consiste en la reconstruccion 3D de la superficie del colon a partir del volumen tomografico. El aporte principal en este paso es el suavizado de la imagen. A partir de la superficie, se detectan aquellas zonas candidatas de ser polipos mediante una estrategia multi-escala que permite delinear con precision la zona. Luego para cada candidato se extraen caracteristicas geometricas y de textura, que son calculadas tambien en el tejido que rodea la zona a efectos de compararlas. Finalmente las zonas candidatas se clasifican utilizando SVM. Los resultados obtenidos son prometedores, permitiendo detectar un 100% de los polipos mayoresColorectal cancer is the second leading cause of cancer-related death in the United
States, and the third cause worldwide. The early detection of polyps is fundamental,
allowing to reduce mortality rates up to 90%. Nowadays, optical colonoscopy is the
most used detection method due in part to its relative high performance.
Virtual Colonoscopy is a promising alternative technique that emerged in the 90's.
It uses volumetric Computed Tomographic data of the cleansed and air-distended colon,
and the examination is made by a specialist from the images in a computer. Therefore,
this technique is less invasive and less expensive than optical colonoscopy, but up to now
the false positive and false negative rates are above the accepted medical limits. Recent
advances in imaging techniques have the potential to reduce these rates; consequently,
we are currently re-experiencing an increasing interest in Virtual Colonoscopy.
In this work we propose a complete pipeline for a Computer-Aided Detection algorithm.
The system starts with a novel and simple segmentation step. We then introduce
geometrical and textural features that take into account not only the candidate polyp
region, but the surrounding area at multiple scales as well. This way, our proposed CAD
algorithm is able to accurately detect candidate polyps by measuring local variations of
these features. Candidate patches are then classi ed using SVM. The whole algorithm is
completely automatic and produces state-of-the-art results, achieving 100% sensitivity
for polyps greater than 6mm in size with less than one false positive per case, and 100%
sensitivity for polyps greater than 3mm in size with 2:2 false positives per case
Segmentation and classification of lung nodules from Thoracic CT scans : methods based on dictionary learning and deep convolutional neural networks.
Lung cancer is a leading cause of cancer death in the world. Key to survival of patients is early diagnosis. Studies have demonstrated that screening high risk patients with Low-dose Computed Tomography (CT) is invaluable for reducing morbidity and mortality. Computer Aided Diagnosis (CADx) systems can assist radiologists and care providers in reading and analyzing lung CT images to segment, classify, and keep track of nodules for signs of cancer. In this thesis, we propose a CADx system for this purpose. To predict lung nodule malignancy, we propose a new deep learning framework that combines Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to learn best in-plane and inter-slice visual features for diagnostic nodule classification. Since a nodule\u27s volumetric growth and shape variation over a period of time may reveal information regarding the malignancy of nodule, separately, a dictionary learning based approach is proposed to segment the nodule\u27s shape at two time points from two scans, one year apart. The output of a CNN classifier trained to learn visual appearance of malignant nodules is then combined with the derived measures of shape change and volumetric growth in assigning a probability of malignancy to the nodule. Due to the limited number of available CT scans of benign and malignant nodules in the image database from the National Lung Screening Trial (NLST), we chose to initially train a deep neural network on the larger LUNA16 Challenge database which was built for the purpose of eliminating false positives from detected nodules in thoracic CT scans. Discriminative features that were learned in this application were transferred to predict malignancy. The algorithm for segmenting nodule shapes in serial CT scans utilizes a sparse combination of training shapes (SCoTS). This algorithm captures a sparse representation of a shape in input data through a linear span of previously delineated shapes in a training repository. The model updates shape prior over level set iterations and captures variabilities in shapes by a sparse combination of the training data. The level set evolution is therefore driven by a data term as well as a term capturing valid prior shapes. During evolution, the shape prior influence is adjusted based on shape reconstruction, with the assigned weight determined from the degree of sparsity of the representation. The discriminative nature of sparse representation, affords us the opportunity to compare nodules\u27 variations in consecutive time points and to predict malignancy. Experimental validations of the proposed segmentation algorithm have been demonstrated on 542 3-D lung nodule data from the LIDC-IDRI database which includes radiologist delineated nodule boundaries. The effectiveness of the proposed deep learning and dictionary learning architectures for malignancy prediction have been demonstrated on CT data from 370 biopsied subjects collected from the NLST database. Each subject in this database had at least two serial CT scans at two separate time points one year apart. The proposed RNN CAD system achieved an ROC Area Under the Curve (AUC) of 0.87, when validated on CT data from nodules at second sequential time point and 0.83 based on dictionary learning method; however, when nodule shape change and appearance were combined, the classifier performance improved to AUC=0.89
Detection and Segmentation of Colonic Polyps on Implicit Isosurfaces by Second Principal Curvature Flow
Todayβs computer aided detection systems for computed tomography colonography (CTC) enable automated detection and segmentation of colorectal polyps.We present a paradigm shift by proposing a method that measures the amount of protrudedness of a candidate object in a scale adaptive fashion. One of the main results is that the performance of the candidate detection depends only on one parameter, the amount of protrusion. Additionally the method yields correct polyp segmentation without the need of an additional segmentation step. The supervised pattern recognition involves a clear distinction between size related features and features related to shape or intensity. A Mahalanobis transformation of the latter facilitates ranking of the objects using a logistic classifier. We evaluate two implementations of the method on 84 patients with a total of 57 polyps larger than or equal to 6 mm.We obtained a performance of 95% sensitivity at four false positives per scan for polyps larger than or equal to 6 mm.Imaging Science and TechnologyApplied Science