Automatic Initialization Of Contour For Level Set Algorithms Guided By Integration Of Multiple Views To Segment Abdominal CT Scans

This paper presents a new automatic initialization procedure for a level-set based segmentation algorithm that works on all slices for a given CT dataset

Automatic Abdominal Organ Segmentation from CT images

In the recent years a great deal of research work has been devoted to the development of semi-automatic and automatic techniques for the analysis of abdominal CT images. Some of the current interests are the automatic diagnosis of liver, spleen, and kidney pathologies and the 3D volume rendering of the abdominal organs. The first and fundamental step in all these studies is the automatic organs segmentation, that is still an open problem. In this paper we propose our fully automatic system that employs a hierarchical gray level based framework to segment heart, bones (i.e. ribs and spine), liver and its blood vessels, kidneys, and spleen. The overall system has been evaluated on the data of 100 patients, obtaining a good assessment both by visual inspection by three experts, and by comparing the computed results to the boundaries manually traced by experts

استخدام تقنيات معالجة الصورة في الاستخلاص الآلي للمناطق المريبة من الكبد في صور الأشعة السينية المقطعية المحوسبة

يهدف هذا البحث إلى تطوير طريقة تقوم بتنفيذ الكشف الآلي بمساعدة الحاسوب عن المناطق المريبة في الكبد. تتألف الطريقة من مرحلتين. يتم في المرحلة الأولى استخدام تقنية ترقيم المركبات المتصلة، وتقنيات العمليات المورفولوحية من أجل الاستخلاص الآلي لمنطقة الكبد في الصورة المقطعية المحوسبة لمنطقة البطن. يتم في المرحلة الثانية استخلاص المناطق المريبة من منطقة الكبد باستخدام خوارزمية تجميع البيانات  FCM. تمت الدراسة على مجموعة من الصور المقطعية المحوسبة لمنطقة البطن، التي تحتوي على 11 منطقة مريبة في الكبد. وقد أعطت الطريقة المطورة نتائج واعدة، حيث تم كشف واستخلاص كل المناطق المريبة آلياً، ولكن اختلفت دقة استخلاص هذه المناطق حسب طبيعة الآفة المريبة في الكبد وموقعها. This research aims at developing a method that can perform automatic computer aided detection of suspicious regions from the liver. The proposed method consists of two phases. In the first phase, the region of the liver is automatically extracted from a CT abdominal image, using connected components labeling technique, and morphological operations. In the second phase, suspicious regions are further extracted from the extracted liver region using the Fuzzy C-Means Clustering (FCM) algorithm. The CT images dataset used for this work comprised 11CT abdominal images contain suspicious areas in the liver regions. The proposed method gave promising results where all suspicious regions were automatically detected and extracted. The accuracy of extraction differed according to the nature and location of each lesion of those detected in the liver

استخدام تقنيات معالجة الصورة في الاستخلاص الآلي للمناطق المريبة من الكبد في صور الأشعة السينية المقطعية المحوسبة

يهدف هذا البحث إلى تطوير طريقة تقوم بتنفيذ الكشف الآلي بمساعدة الحاسوب عن المناطق المريبة في الكبد. تتألف الطريقة من مرحلتين. يتم في المرحلة الأولى استخدام تقنية ترقيم المركبات المتصلة، وتقنيات العمليات المورفولوحية من أجل الاستخلاص الآلي لمنطقة الكبد في الصورة المقطعية المحوسبة لمنطقة البطن. يتم في المرحلة الثانية استخلاص المناطق المريبة من منطقة الكبد باستخدام خوارزمية تجميع البيانات  FCM. تمت الدراسة على مجموعة من الصور المقطعية المحوسبة لمنطقة البطن، التي تحتوي على 11 منطقة مريبة في الكبد. وقد أعطت الطريقة المطورة نتائج واعدة، حيث تم كشف واستخلاص كل المناطق المريبة آلياً، ولكن اختلفت دقة استخلاص هذه المناطق حسب طبيعة الآفة المريبة في الكبد وموقعها. This research aims at developing a method that can perform automatic computer aided detection of suspicious regions from the liver. The proposed method consists of two phases. In the first phase, the region of the liver is automatically extracted from a CT abdominal image, using connected components labeling technique, and morphological operations. In the second phase, suspicious regions are further extracted from the extracted liver region using the Fuzzy C-Means Clustering (FCM) algorithm. The CT images dataset used for this work comprised 11CT abdominal images contain suspicious areas in the liver regions. The proposed method gave promising results where all suspicious regions were automatically detected and extracted. The accuracy of extraction differed according to the nature and location of each lesion of those detected in the liver

Generalizable deep learning based medical image segmentation

Deep learning is revolutionizing medical image analysis and interpretation. However, its real-world deployment is often hindered by the poor generalization to unseen domains (new imaging modalities and protocols). This lack of generalization ability is further exacerbated by the scarcity of labeled datasets for training: Data collection and annotation can be prohibitively expensive in terms of labor and costs because label quality heavily dependents on the expertise of radiologists. Additionally, unreliable predictions caused by poor model generalization pose safety risks to clinical downstream applications. To mitigate labeling requirements, we investigate and develop a series of techniques to strengthen the generalization ability and the data efficiency of deep medical image computing models. We further improve model accountability and identify unreliable predictions made on out-of-domain data, by designing probability calibration techniques. In the first and the second part of thesis, we discuss two types of problems for handling unexpected domains: unsupervised domain adaptation and single-source domain generalization. For domain adaptation we present a data-efficient technique that adapts a segmentation model trained on a labeled source domain (e.g., MRI) to an unlabeled target domain (e.g., CT), using a small number of unlabeled training images from the target domain. For domain generalization, we focus on both image reconstruction and segmentation. For image reconstruction, we design a simple and effective domain generalization technique for cross-domain MRI reconstruction, by reusing image representations learned from natural image datasets. For image segmentation, we perform causal analysis of the challenging cross-domain image segmentation problem. Guided by this causal analysis we propose an effective data-augmentation-based generalization technique for single-source domains. The proposed method outperforms existing approaches on a large variety of cross-domain image segmentation scenarios. In the third part of the thesis, we present a novel self-supervised method for learning generic image representations that can be used to analyze unexpected objects of interest. The proposed method is designed together with a novel few-shot image segmentation framework that can segment unseen objects of interest by taking only a few labeled examples as references. Superior flexibility over conventional fully-supervised models is demonstrated by our few-shot framework: it does not require any fine-tuning on novel objects of interest. We further build a publicly available comprehensive evaluation environment for few-shot medical image segmentation. In the fourth part of the thesis, we present a novel probability calibration model. To ensure safety in clinical settings, a deep model is expected to be able to alert human radiologists if it has low confidence, especially when confronted with out-of-domain data. To this end we present a plug-and-play model to calibrate prediction probabilities on out-of-domain data. It aligns the prediction probability in line with the actual accuracy on the test data. We evaluate our method on both artifact-corrupted images and images from an unforeseen MRI scanning protocol. Our method demonstrates improved calibration accuracy compared with the state-of-the-art method. Finally, we summarize the major contributions and limitations of our works. We also suggest future research directions that will benefit from the works in this thesis.Open Acces

Intelligent X-ray imaging inspection system for the food industry.

The inspection process of a product is an important stage of a modern production factory. This research presents a generic X-ray imaging inspection system with application for the detection of foreign bodies in a meat product for the food industry. The most important modules in the system are the image processing module and the high-level detection system. This research discusses the use of neural networks for image processing and fuzzy-logic for the detection of potential foreign bodies found in x-ray images of chicken breast meat after the de-boning process. The meat product is passed under a solid-state x-ray sensor that acquires a dual-band two-dimensional image of the meat (a low- and a high energy image). A series of image processing operations are applied to the acquired image (pre-processing, noise removal, contrast enhancement). The most important step of the image processing is the segmentation of the image into meaningful objects. The segmentation task is a difficult one due to the lack of clarity of the acquired X-ray images and the resulting segmented image represents not only correctly identified foreign bodies but also areas caused by overlapping muscle regions in the meat which appear very similar to foreign bodies in the resulting x-ray image. A Hopfield neural network architecture was proposed for the segmentation of a X-ray dual-band image. A number of image processing measurements were made on each object (geometrical and grey-level based statistical features) and these features were used as the input into a fuzzy logic based high-level detection system whose function was to differentiate between bones and non-bone segmented regions. The results show that system's performance is considerably improved over non-fuzzy or crisp methods. Possible noise affecting the system is also investigated. The proposed system proved to be robust and flexible while achieving a high level of performance. Furthermore, it is possible to use the same approach when analysing images from other applications areas from the automotive industry to medicine

Intelligent X-ray imaging inspection system for the food industry.

The inspection process of a product is an important stage of a modern production factory. This research presents a generic X-ray imaging inspection system with application for the detection of foreign bodies in a meat product for the food industry. The most important modules in the system are the image processing module and the high-level detection system. This research discusses the use of neural networks for image processing and fuzzy-logic for the detection of potential foreign bodies found in x-ray images of chicken breast meat after the de-boning process. The meat product is passed under a solid-state x-ray sensor that acquires a dual-band two-dimensional image of the meat (a low- and a high energy image). A series of image processing operations are applied to the acquired image (pre-processing, noise removal, contrast enhancement). The most important step of the image processing is the segmentation of the image into meaningful objects. The segmentation task is a difficult one due to the lack of clarity of the acquired X-ray images and the resulting segmented image represents not only correctly identified foreign bodies but also areas caused by overlapping muscle regions in the meat which appear very similar to foreign bodies in the resulting x-ray image. A Hopfield neural network architecture was proposed for the segmentation of a X-ray dual-band image. A number of image processing measurements were made on each object (geometrical and grey-level based statistical features) and these features were used as the input into a fuzzy logic based high-level detection system whose function was to differentiate between bones and non-bone segmented regions. The results show that system's performance is considerably improved over non-fuzzy or crisp methods. Possible noise affecting the system is also investigated. The proposed system proved to be robust and flexible while achieving a high level of performance. Furthermore, it is possible to use the same approach when analysing images from other applications areas from the automotive industry to medicine