Pulmonary Vascular Tree Segmentation from Contrast-Enhanced CT Images

We present a pulmonary vessel segmentation algorithm, which is fast, fully automatic and robust. It uses a coarse segmentation of the airway tree and a left and right lung labeled volume to restrict a vessel enhancement filter, based on an offset medialness function, to the lungs. We show the application of our algorithm on contrast-enhanced CT images, where we derive a clinical parameter to detect pulmonary hypertension (PH) in patients. Results on a dataset of 24 patients show that quantitative indices derived from the segmentation are applicable to distinguish patients with and without PH. Further work-in-progress results are shown on the VESSEL12 challenge dataset, which is composed of non-contrast-enhanced scans, where we range in the midfield of participating contestants.Comment: Part of the OAGM/AAPR 2013 proceedings (1304.1876

Bilgisayarlı tomografi anjiyografi görüntülerinde pulmoner embolilerin bilgisayar destekli tespiti

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Dolaşım sistemi problemlerinden biri olan PE’ler, akciğer atardamarı veya onun dallarından bir ya da birkaçının kan pıhtısı ile tıkanması sonucu ortaya çıkan klinik tablodur. Tanısı güç olan bir hastalık olup, erken tanı ve tedavisi hayat kurtarıcıdır. Bu tez çalışmasında; BTA görüntülerinden tanısı konulabilen PE’lerin, BDT ile belirlenmesi işlemi gerçekleştirilmiştir. Literatürde PE’lerin BDT ile belirlenmesinde iki farklı yaklaşım mevcuttur. Bunlardan birincisi akciğer damar ağacının çıkartılması ile PE’lerin belirlenmesi. İkincisi ise akciğer damar ağacı çıkartmadan kızaklama (tobogganing) yöntemi ile PE’lerin belirlenmesidir. Birinci yöntem ile yapılan çalışmalarda, ilk olarak akciğer damarları, yüksek yoğunluk değerleri ile bölütlenmeye çalışılmıştır. Ancak PE olan bölgelerde damar yapısı bozuk olacağından, ikinci olarak damar yapısını düzgün devam ettirmek için PE’ler, bulundukları bölgede, kendileri ile aynı düşük yoğunluğa sahip dokulardan, boyut ve biçimsel özelliklerine göre ayırt edilmeye çalışılmıştır. Daha sonra damarlar ile PE’ler, iz takibi yöntemiyle ya da damarların; bölgesel, biçimsel, hacimsel özelliklerine bakılarak birleştirilmişlerdir. Böylece akciğer damar bölütlemesi ile PE’ler belirlenmeye çalışılmıştır. Ancak PE’lerin sabit bir biçimi yoktur. Bölgesel olarak damar ağacı içerisinde herhangi biryerde olabilir. Boyut olarak da belirli bir boyutun altı PE olarak değerlendirilmez, üstü ise değişken boyutlarda olabilmektedir. Ayrıca, damarlar tamamen tıkanmışsa, BTA görüntülerinde kopuk görüneceğinden, iz takibi yapmak zordur. Bu nedenle daha önce yapılmış çalışmalardaki yöntemlerin performansları düşüktür. Bu tez çalışmasında yeni bir yöntem geliştirilmiştir. İlk adımda akciğer bölütlemesi gerçekleştirilmiştir. İkinci adımda damar bölütlemede kullanılmak üzere, BTA görüntülerinde, anatomik yapının değişmeyen özelliklerinden yararlanarak bazı referans noktalar (RN) belirlenmiştir. Bu RN’lar sayesinde truncus, sol-sağ pulmoner arter, lobar segmenter damarlar, PE’lerle birlikte bölütlenmiştir. RN’ları ile PE’ler, aynı yoğunluklu diğer dokulardan ve eğer varsa PE dışında akciğer ya da kalp hastalığı dokularından, ayırt edilebilmiştir. Subsegmenter damarlar da akciğer bölgesindeki yoğunluk farklarından PE’ler ile birlikte belirlenmiştir. Son olarak tüm damarlar birleştirilerek akciğer damar ağacı çıkartılmıştır. Üçüncü adımda damar ağacından, PE’ler; damarların iç bölgesinde olacak şekilde, yoğunluk farkları incelenip, belirli bir boyutun altındaki bileşenler görüntülerden kaldırılarak belirlenmiştir. Bu işlemlerin sonunda elde edilen sonuçlar, tıbbi görüntü değerlendirmelerinde kullanılan performans ölçümleri ile analiz edilerek, daha önce yapılmış çalışmalarla karşılaştırılmıştır. PE tespiti için bu tez kapsamında geliştirilen yöntemin performansının literatürdeki yöntemlerden daha iyi olduğu ve tıbbi açıdan hekimlere oldukça yeterli sonuçlar verdiği belirlenmiştir.PE, one of the circulatory system problems, is the clinical result at the end of that the lung artery or one or a few branches of them are bunged up by clot of blood. Besides it is a hard disease to diagnose, early diagnosis and treatment of it can save lives. The detection by CAD of PEs which can be diagnosed from CTA Images has been carried out at this thesis work. There are two different approaches to detect PEs by CAD in literature. The first one is to detect PEs by indicating lung vessel tree. The second one is to detect PEs by tobogganing method without indicating lung vessel tree. The lung vessels firstly have been segmented with high intensity values at the works done with the first method. However, because the vessel structure will be damaged in PE regions, secondly to continue the vessel structure smoothly, PEs have been distinguished from the tissues with the same low intensity like them in their regions according to their sizes and shapes. Then, vessels and PE have been connected by tracking method or by looking at region, shape and volume properties of the vessels. However, there is no a constant shape of PEs. It can be anywhere in the vessel tree regionally. It isn’t be considered as PE under a certain size, though it can be in different size above it. Besides, if the vessels are bunged completely, it will be hard to do tracking so that it seems unconnected in CTA images. So the performances of the methods in previous works are lower. A new method has been developed at this thesis work. firstly, lung segmentation has been carried out. Secondly, by making use of the unchangeable properties of anatomical structure in CTA images, some reference points (RP) have been detected in order to use at vessel segmentation. Due to these RPs, truncus, left-right pulmonary artery, lobar segment vessels have been segmented with the PE. RP and PE have been distinguished from the other tissues having the same intensity and from the lung and heart disease tissues except for PE. Subsegment vessels have been detected with PE by looking at the differences of intensities in lung region. Lastly, lung vessel tree has been detected by connecting all of the vessels. Thirdly, PEs have been detected from the vessel tree, by examining the intensity differences in a way of inside regions of vessel and removing the components under a certain size from the image. The results at the end of these processes have been analyzed by the performance measures which are used in medical image evaluation and compared to the previous works. It has been seen that the performance of the method for PE detection at this thesis is better than the ones in literature and it gives rather enough results to the surgeries medically

Decision analysis in the clinical neurosciences

Diagnostic and therapeutic choice in neurology can fortunately be made without formal decision support in the majority of cases. in many patients a diagnosis and treatment choice are relatively easy to establish. This study however, concerns the application of a decision support methodology - clinical decision analysis - to several problems in the clinical neurosdences where diagnosis, prognosis and therapeutic choice are not obvious. Sometimes decision making in clinical medicine can be extremely difficult There may be large interests atstake,and theamount of information that has to beprocessed can be enormous. Data from the patient's history, physical examination, diagnostic procedures, clinical knowledge and the scientific information have to be combined in order to arrive at a prognosis and to develop a diagnostic and therapeutic strategy. Add to this that most diagnostic tests are not completely accurate, that therapy is not always and entirely effective, that diagnostic and therapeutic procedures may be risky, unpleasant, expensive and time-consuming, and that prognosis is most of the times uncertain. The decision process itself is limited by time and by budgetary constraints. The clinician has to recognize situations where the patient's preferences are important, and he has to know when the clinical situation needs a doctor - patient relationship characterized by activity - passivity, guidance - cooperation or mutual participation. Moreover, physicians and their patients (as any human being) find it difficult to handle uncertainty.'" Oinicians often discuss the pro' s and con' sof altemativemanagementstrategies with their senior and junior colleagues, but a language that effectively and explicitly addresses uncertainty and preferences for health outcomes is not part of the physician's standard equipment. Several other factors influence the decision process as welL It has been demonstrated that patient characteristics, (such as social class), physician's personal characteristics (such as age, type of specialty), and the physician's interaction with his profession (for example whether he is in a solo- of group-practice) all may be of influence

MR image based measurement, modelling and diagnostic interpretation of pressure and flow in the pulmonary arteries: applications in pulmonary hypertension

Pulmonary hypertension (PH) is a clinical condition characterised by an increased mean pulmonary arterial pressure (mPAP) of over 25 mmHg measured, at rest, by right heart catheterisation (RHC). RHC is currently considered the gold standard for diagnosis, follow-up and measurement of response to treatment. Although the severe complications and mortality risk associated with the invasive procedure are reduced when it is performed in a specialist centre, finding non-invasive PH diagnosis methods is highly desirable. Non-invasive, non-ionising imaging techniques, based on magnetic resonance imaging (MRI) and on echocardiography, have been integrated into the clinical routine as means for PH assessment. Although the imaging techniques can provide valuable information supporting the PH diagnosis, accurately identifying patients with PH based upon images alone remains challenging. Computationally based models can bring additional insights into the haemodynamic changes occurring under the manifestation of PH. The primary hypothesis of this thesis is that that the physiological status of the pulmonary circulation can be inferred using solely non-invasive flow and anatomy measurements of the pulmonary arteries, measured by MRI and interpreted by 0D and 1D mathematical models. The aim was to implement a series of simple mathematical models, taking the inputs from MRI measurements, and to evaluate their potential to support the non-invasive diagnosis and monitoring of PH. The principal objective was to develop a tool that can readily be translated into the clinic, requiring minimum operator input and time and returning meaningful and accurate results. Two mathematical models, a 3 element Windkessel model and a 1D model of an axisymmetric straight elastic tube for wave reflections were implemented and clinically tested on a cohort of healthy volunteers and of patients who were clinically investigated for PH. The latter group contained some who were normotensive, and those with PH were stratified according to severity. A 2D semi-automatic image segmentation workflow was developed to provide patient specific, simultaneous flow and anatomy measurements of the main pulmonary artery (MPA) as input to the mathematical models. Several diagnostic indices are proposed, and of these distal resistance (Rd), total vascular compliance (C) and the ratio of reflected to total wave power (Wb/Wtot) showed statistically significant differences between the analysed groups, with good accuracy in PH classification. A machine learning classifier using the derived computational metrics and several other PH metrics computed from MRI images of the MPA and of the right ventricle alone, proposed in the literature as PH surrogate markers, was trained and validated with leave-one-out cross-validation to improve the accuracy of non-invasive PH diagnosis. The results accurately classified 92% of the patients, and furthermore the misclassified 8% were patients with mPAP close to the 25 mmHg (at RHC) threshold (within the range of clinical uncertainty). The individual analysis of all PH surrogate markers emphasised that wave reflection quantification, although with lower diagnosis accuracy (75%) than the machine learning model embedding multiple markers, has the potential to distinguish between multiple PH categories. A finite element method (FEM) based model to solve a 1D pulmonary arterial tree linear system, has been implemented to contribute further to the accurate, non-invasive assessment of pulmonary hypertension. The diagnostic protocols, including the analysis work flow, developed and reported in this PhD thesis can be integrated into the clinical process, with the potential to reduce the need for RHC by maximising the use of available MRI data