A Robust and Fast System for CTC Computer-Aided Detection of Colorectal Lesions

We present a complete, end-to-end computer-aided detection (CAD) system for identifying lesions in the colon, imaged with computed tomography (CT). This system includes facilities for colon segmentation, candidate generation, feature analysis, and classification. The algorithms have been designed to offer robust performance to variation in image data and patient preparation. By utilizing efficient 2D and 3D processing, software optimizations, multi-threading, feature selection, and an optimized cascade classifier, the CAD system quickly determines a set of detection marks. The colon CAD system has been validated on the largest set of data to date, and demonstrates excellent performance, in terms of its high sensitivity, low false positive rate, and computational efficiency

Development of a synthetic phantom for the selection of optimal scanning parameters in CAD-CT colonography

The aim of this paper is to present the development of a synthetic phantom that can be used for the selection of optimal scanning parameters in computed tomography (CT) colonography. In this paper we attempt to evaluate the influence of the main scanning parameters including slice thickness, reconstruction interval, field of view, table speed and radiation dose on the overall performance of a computer aided detection (CAD)–CTC system. From these parameters the radiation dose received a special attention, as the major problem associated with CTC is the patient exposure to significant levels of ionising radiation. To examine the influence of the scanning parameters we performed 51 CT scans where the spread of scanning parameters was divided into seven different protocols. A large number of experimental tests were performed and the results analysed. The results show that automatic polyp detection is feasible even in cases when the CAD–CTC system was applied to low dose CT data acquired with the following protocol: 13 mAs/rotation with collimation of 1.5 mm × 16 mm, slice thickness of 3.0 mm, reconstruction interval of 1.5 mm, table speed of 30 mm per rotation. The CT phantom data acquired using this protocol was analysed by an automated CAD–CTC system and the experimental results indicate that our system identified all clinically significant polyps (i.e. larger than 5 mm)

Искусственный интеллект при колоректальном раке: обзор

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.Цель исследования - оценка возможностей использования искусственного интеллекта (ИИ) в диагностике, лечении и прогнозировании колоректального рака (КРР), а также обсуждение потенциала ИИ в лечении КРР. Материал и методы. Проведен поиск научных публикаций в поисковых системах Web of Science, Scopus, PubMed, Medline и eLIBRARY. Было просмотрено более 100 источников по применению ИИ для диагностики, лечения и прогнозирования КРР. В обзор включены данные из 83 статей. Результаты. Проведен анализ литературы, посвященной применению искусственного интеллекта в медицине, особое внимание уделено его использованию при колоректальном раке. Обсуждаются этапы развития ИИ при КРР, включая молекулярную верификацию, лучевую диагностику, разработку лекарств и индивидуальное лечение. Подчеркнуты преимущества ИИ в анализе медицинских изображений, таких как КТ, МРТ и ПЭТ, что повышает точность диагностики. Рассматриваются такие проблемы развития ИИ, как стандартизация данных и интерпретируемость алгоритмов машинного обучения. Подчеркивается роль ИИ в выборе оптимальной тактики лечения и повышении эффективности хирургического вмешательства. Учитываются этические и нормативные аспекты ИИ, включая доверие пациентов, безопасность данных и ответственность в проведении операций с использованием ИИ. Обсуждаются преимущества ИИ в диагностике, лечении и прогнозировании колоректального рака, проблемы и перспективы улучшения результатов лечения

A robust and efficient approach to detect 3D rectal tubes from CT colonography

Purpose: The rectal tube (RT) is a common source of false positives (FPs) in computer-aided detection (CAD) systems for CT colonography. A robust and efficient detection of RT can improve CAD performance by eliminating such “obvious” FPs and increase radiologists’ confidence in CAD. Methods: In this paper, we present a novel and robust bottom-up approach to detect the RT. Probabilistic models, trained using kernel density estimation on simple low-level features, are employed to rank and select the most likely RT tube candidate on each axial slice. Then, a shape model, robustly estimated using Random Sample Consensus (RANSAC), infers the global RT path from the selected local detections. Subimages around the RT path are projected into a subspace formed from training subimages of the RT. A quadratic discriminant analysis (QDA) provides a classification of a subimage as RT or non-RT based on the projection. Finally, a bottom-top clustering method is proposed to merge the classification predictions together to locate the tip position of the RT. Results: Our method is validated using a diverse database, including data from five hospitals. On a testing data with 21 patients (42 volumes), 99.5% of annotated RT paths have been successfully detected. Evaluated with CAD, 98.4% of FPs caused by the RT have been detected and removed without any loss of sensitivity. Conclusion: The proposed method demonstrates a high detection rate of the RT path, and when tested in a CAD system, reduces FPs caused by the RT without the loss of sensitivity

Performance and Evaluation in Computed Tomographic Colonography Screening for Colorectal Cancer

Each year over 20,000 people die from colorectal cancer (CRC). However, despite causing the second highest number of cancer deaths, CRC is not only curable if detected early but can be prevented by population screening. The detection and removal of pre-malignant polyps in the colon prevents cancer from ever developing. As such, screening of the at-risk population (those over 45-50 years) confers protection against CRC incidence and mortality. Although the principles and benefit of screening are well established, the adequate provision of screening is a complex process requiring robust healthcare infrastructure, evidence-based quality assurance and resources. The success of any screening programme is dependent on the accuracy of the screening investigations deployed and sufficiently high uptake by the target population. In England, the Bowel Cancer Screening Programme (BCSP) delivers screening via initial stool testing to triage patients for the endoscopic procedure, colonoscopy, or the radiological investigation CT colonography (CTC) in some patients. There has been considerable investment in colonoscopy accreditation processes which contribute to high quality services, suitable access for patients and a competent endoscopy workforce. The performance of colonoscopists in the BCSP is tightly monitored and regulated; however, the same is not true for CTC. Comparatively, there has been little investment in CTC services, and in fact there is no mandatory accreditation or centralised training. Instead, CTC reporting radiologists must learn ad hoc on the job, or at self-funded commercial workshops. This inevitably leads to variability in quality and expertise, inequity in service provision, and could negatively impact patient outcomes. To address this disparity and develop evidence-based training, one must determine what factors affect the performance of CTC reporting radiologists, what CTC training is necessary, and what training works. This thesis investigates these topics and is structured as follows: Section A reviews the background literature, describing the public health burden of CRC and the role of screening. Aspects of CTC screening and its role in the BCSP are explored. The importance of performance monitoring and value of accreditation are examined and the disparity between CTC, colonoscopy and other imaging-based screening programmes is discussed. Section B expands on radiologist performance by determining the post-imaging CRC (or interval cancer) rate through systematic review and meta-analysis. Factors contributing to the interval cancer rate are evaluated, and an observational study assessing factors affecting CTC accuracy is presented. The impact of CTC training is assessed via a structured review and best principles for training delivery are discussed. Section C presents a multicentre, cluster-randomised control trial developed from the data and understanding described in Sections A and B. Section D summarises the thesis and discusses future recommendations and research

Feature Selection for Computer-Aided Polyp Detection using MRMR

In building robust classifiers for computer-aided detection (CAD) of lesions, selection of relevant features is of fundamental importance. Typically one is interested in determining which, of a large number of potentially redundant or noisy features, are most discriminative for classification. Searching all possible subsets of features is impractical computationally. This paper proposes a feature selection scheme combining AdaBoost with the Minimum Redundancy Maximum Relevance (MRMR) to focus on the most discriminative features. A fitness function is designed to determine the optimal number of features in a forward wrapper search. Bagging is applied to reduce the variance of the classifier and make a reliable selection. Experiments demonstrate that by selecting just 11 percent of the total features, the classifier can achieve better prediction on independent test data compared to the 70 percent of the total features selected by AdaBoost

Automatic Rectum Limit Detection by Anatomical Markers Correlation

Several diseases take place at the end of the digestive system. Many of them can be diagnosed by means of different medical imaging modalities together with computer aided detection (CAD) systems. These CAD systems mainly focus on the complete segmentation of the digestive tube. However, the detection of limits between different sections could provide important information to these systems. In this paper we present an automatic method for detecting the rectum and sigmoid colon limit using a novel global curvature analysis over the centerline of the segmented digestive tube in different imaging modalities. The results are compared with the gold standard rectum upper limit through a validation scheme comprising two different anatomical markers: the third sacral vertebra and the average rectum length. Experimental results in both magnetic resonance imaging (MRI) and computed tomography colonography (CTC) acquisitions show the efficacy of the proposed strategy in automatic detection of rectum limits. The method is intended for application to the rectum segmentation in MRI for geometrical modeling and as contextual information source in virtual colonoscopies and CAD systems.Fil: Namias, Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; ArgentinaFil: Venere, Marcelo Javier. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: del Fresno, Mirta Mariana. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados; Argentina. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; ArgentinaFil: D'amato, Juan Pablo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados; Argentina. Comision Nacional de Energi­a Atomica. Centro Atomico Bariloche; Argentin