Automated detection of new multiple sclerosis lesions in longitudinal magnetic resonance imaging
Başlık çevirisi mevcut değil.
- Tez No: 602595
- Danışmanlar: DR. XAVIER LLADO
- Tez Türü: Doktora
- Konular: Nöroloji, Radyoloji ve Nükleer Tıp, Neurology, Radiology and Nuclear Medicine
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2014
- Dil: İngilizce
- Üniversite: Unıversıtat de Gırona
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: Belirtilmemiş.
Özet
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Özet (Çeviri)
This thesis deals with the detection of new multiple sclerosis (MS) lesions in longitudinal brain magnetic resonance (MR) imaging. This disease is characterized by the presence of lesions in the brain, predominantly in the white matter (WM) tissue of the brain. The detection and quantification of new lesions are crucial to follow-up MS patients. Moreover, the manual detection of these new lesions is not only time-consuming, but is also prone to intra- and inter-observer variability. Therefore, the development of automated techniques for the detection MS lesions is a major challenge. After a thorough analysis of the state-of-the art in MS lesion detection approaches, we present a new classification of techniques pointing out their main strengths and weaknesses. A complementary quantitative evaluation of some of the most remarkable methods in the literature is also provided. Subsequently, we present a new proposal based on a change detection approach, which combines various characteristics of different MR image modalities. Firstly, several preprocessing methods are included in the pipeline to improve the quality of MR images. We analyze these processes as well as several rigid and non-rigid image registration methods in detail. The subtraction of the baseline and follow-up images is used to determine changes between the images. Moreover, we apply a WM masking step in order to reduce the search space for lesions only within WM. Afterwards, we apply a threshold to the subtraction images. Although determining the threshold can be done by experts, we propose an automated thresholding process which provides a satisfactory trade-off between sensitivity and specificity. Finally, we refine the candidate lesions detected using lesion features, particularly in order to reduce false positive lesions. For this purpose, including the baseline and follow-up images, we join both results obtained from PD-w and T2-w images in a supervised and an unsupervised manner. Experimental results are evaluated on a database of 20 MS patients with a variable lesion load, where manual segmentation provided by experts was available. The evaluation, carried out in a quantitative and qualitative manner, includes a comparison and uses several metrics for detection and segmentation.
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