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PCcS-RAU-Net: Automated parcellated Corpus callosum segmentation from brain MRI images using modified residual attention U-Net

Chandra Anjali, Verma Shrish, Raghuvanshi A.S., Kuber Bodhey Narendra

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 2

403--427

EN

Background: The Corpus callosum (Cc) in the cerebral cortex is a bundle of neural fibers that facilitates inter-hemispheric communication. The Cc area and area of its sub-regions (also known as parcels) have been examined as a biomarker for cortical pathology and differential diagnosis in neurodegenerative diseases such as Autism, Alzheimer’s disease (AD), and more. Manual segmentation and parcellation of Cc are laborious and time-consuming. The present work proposes a novel work of automated parcellated Cc (PCc) segmentation that will serve as a potential biomarker to study and diagnose neurological disorders in brain MRI images. Method: In this perspective, the present work aims to develop an automated PCc segmentation from mid-sagittal T1- weighted (w) 2D brain MRI images using a deep learning-based fully convolutional network, a modified residual attention U-Net, referred to as PCcS-RAU-Net. The model has been modified to use a multi-class segmentation configuration with five target classes (parcels): rostrum, genu, mid-body, isthmus and splenium. Results: The experimental research uses two benchmark MRI datasets, ABIDE and OASIS. The proposed PCcS-RAU-Net outperformed existing methods on the ABIDE dataset with a DSC of 97.10% and MIoU of 94.43%. Furthermore, the model’s performance is validated on the OASIS and Real clinical image (RCI) data and hence verifies the model’s generalization capability. Conclusion: The proposed PCcS-RAU-Net model extracts essential characteristics such as the total area of the Cc (TCcA) to categorize MRI slices into healthy controls (HC) and disease groups. Also, sub-regional areas, Cc1A to Cc5A, help study atrophy progression for early diagnosis.

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Spektroskopia mössbauerowska jako narzędzie do badania kosmosu i mózgu

Duda Przemysław

Prace Naukowe Politechniki Warszawskiej. Fizyka

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2021

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z. 60

3--115

PL

W niniejszej pracy przedstawione zostały wyniki badań meteorytów i próbek biologicznych, przeprowadzonych w ostatnim czasie w kierowanym przeze mnie Laboratorium Spektroskopii Mössbauerowskiej. Badania te dotyczyły między innymi opracowania nowej metody pozwalającej na dokonywanie wstępnej klasyfikacji chondrytów zwyczajnych. Równolegle do badań dotyczących meteorytów zostało zaproponowane użycie spektroskopii mössbauerowskiej do badania próbek biologicznych. Widma mössbauerowskie chondrytów zwyczajnych składają się z dwóch dubletów ze względu na obecność żelaza paramagnetycznego w oliwinach i piroksenach oraz dwóch sekstetów pochodzących od magnetycznie uporządkowanego żelaza obecnego w fazach metalicznych i troilicie. Powierzchnie spektralne różnych faz mineralogicznych w meteorytach, określone przez zastosowanie spektroskopii mössbauerowskiej, są proporcjonalne do liczby atomów żelaza w danej fazie mineralogicznej. Ta właściwość widm mössbauerowskich stanowiła podstawę do skonstruowania metody klasyfikacji chondrytów zwyczajnych. Metoda ta wykorzystuje pola powierzchni spektralnych widm mössbauerowskich, które analizowane są za pomocą wielowymiarowej analizy dyskryminacyjnej i odległości Mahalanobis. Metoda ta nosi nazwę 4M i pozwala określić prawdopodobieństwo przynależności chondrytu zwyczajnego do danego typu - H, L lub LL. Spektroskopia mössbauerowska nie jest rutynowo stosowana do oznaczania stężenia żelaza. Ponieważ jednak ta metoda nie wymaga wstępnej obróbki próbek przed pomiarem, może mieć ona ogromne znaczenie dla oceny stężenia żelaza w próbkach, które można następnie wykorzystać do dalszych badań. Próbki biologiczne są tego dobrym przykładem. Uważa się, że żelazo może odgrywać ważną rolę w neurodegeneracji. W pracy przedstawione zostały wyniki badań porównawczych obszarów ludzkiego mózgu (kontrolnych i patologicznych), przeprowadzonych za pomocą technik spektroskopii mössbauerowskiej i obrazowania metodą rezonansu magnetycznego. Spektroskopia mössbauerowska wykazała wyższe stężenie żelaza w atypowym parkinsonizmie (nazywanym postępującym porażeniem nadjądrowym) w obszarach mózgu takich jak istota czarna (substantia nigra) oraz gałka blada (globus pallidus) w stosunku do próbek stanowiących grupę kontrolną. W pozostałych chorobach neurodegeneracyjnych nie zarejestrowano wzrostu stężenia żelaza w tkankach mózgowych. Ze względu na fakt, że określenie roli żelaza może wnieść bardzo wiele w zrozumienie mechanizmów powstawania i rozwoju chorób neurodegeneracyjnych, badania mössbauerowskie próbek mózgowych stanowią ciekawy i perspektywiczny kierunek badań, który wymaga przeprowadzenia dalszych pomiarów i analiz.

EN

This work presents the recent research results related to meteorites and biological samples conducted in the Mössbauer Spectroscopy Laboratory led by the Author. These studies concerned, among others, the development of a new method allowing for the preliminary classification of ordinary chondrites. Parallel to the research on meteorites, it was proposed to use Mössbauer spectroscopy to study biological samples. The Mössbauer spectra of ordinary chondrites consist of two doublets due to the presence of paramagnetic iron in olivines and pyroxenes and two sextets derived from magnetically ordered iron present in metallic and troilite phases. The spectral areas of various mineralogical phases in meteorites; determined by the use of Mössbauer spectroscopy; are proportional to the number of iron atoms in this mineralogical phase. This property of the Mössbauer spectra formed the basis for constructing a method for classifying ordinary chondrites. This method uses the spectral surface areas of the Mössbauer spectra, which are analysed using multidimensional discriminant analysis and Mahalanobis distances. This method is called 4M and allows one to determine the probability of belonging to one of the types of ordinary chondrites - H, L or LL. Mössbauer spectroscopy in not routinely used to determine iron concentration. However, as this method does not require pre-treatment of samples prior to measurements, it can be of great importance for assessing iron in samples that can then be used for further testing. Here, biological samples are a good example. It is believed that iron can play an important role in neurodegeneration. Thus, the work presents the results of comparative studies of areas of the human brain (control and pathological) carried out using Mössbauer spectroscopy techniques and magnetic resonance imaging. Mössbauer spectroscopy showed a higher concentration of iron in atypical parkinsonism (called progressive supranuclear palsy) in areas of the brain such as substantia nigra and globus pallidus compared to control group samples. In other neurodegenerative diseases, no increase in iron concentration in brain tissues was recorded. Due to the fact that determining the role of iron can contribute a lot to understanding the mechanisms of the formation and development of neurodegenerative diseases, Mössbauer studies of brain samples are an interesting direction of research that requires further measurements and analyses.

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Comparison of Different Data Mining Methods to Determine Disease Progression in Dissimilar Groups of Parkinson’s Patients

Przybyszewski Andrzej W., Chudzik Artur, Szlufik Stanisław, Habela Piotr, Koziorowski Dariusz M.

Fundamenta Informaticae

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2020

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Vol. 176, nr 2

167--181

EN

Parkinson’s disease (PD) is the second after Alzheimer’s most popular neurodegenerative disease (ND). Cures for both NDs are currently unavailable. OBJECTIVE: The purpose of our study was to predict the results of different PD patients’ treatments in order to find an optimal one. METHODS: We have compared rough sets (RS) and others, in short, machine learning (ML) models to describe and predict disease progression expressed as UPDRS values (Unified Parkinson’s Disease Rating Scale) in three groups of Parkinson’s patients: 23 BMT (Best Medical Treatment) patients on medication; 24 DBS patients on medication and on DBS therapy (Deep Brain Stimulation) after surgery performed during our study; and 15 POP (Postoperative) patients who had had surgery earlier (before the beginning of our research). Every PD patient had three visits approximately every six months. The first visit for DBS patients was before surgery. On the basis of the following condition attributes: disease duration, saccadic eye movement parameters, and neuropsychological tests: PDQ39 (Parkinson’s Disease Questionnaire - disease-specific health-related quality-of-life questionnaire), and Epworth Sleepiness Scale tests we have estimated UPDRS changes (as the decision attribute). RESULTS: By means of RS rules obtained for the first visit of BMT/DBS/POP patients, we have predicted UPDRS values in the following year (two visits) with global accuracy of 70% for both BMT visits; 56% for DBS, and 67%, 79% for POP second and third visits. The accuracy obtained by ML models was generally in the same range, but it was calculated separately for different sessions (MedOFF/MedON). We have used RS rules obtained in BMT patients to predict UPDRS of DBS patients; for the first session DBSW1: global accuracy was 64%, for the second DBSW2: 85% and the third DBSW3: 74% but only for DBS patients during stimulation-ON. ML models gave better accuracy for DBSW1/W2 session S1(MedOFF): 88%, but inferior results for session S3 (MedON): 58% and 54%. Both RS and ML could not predict UPDRS in DBS patients during stimulation-OFF visits because of differences in UPDRS. By using RS rules from BMT or DBS patients we could not predict UPDRS of POP group, but with certain limitations (only for MedON), we derived such predictions for the POP group from results of DBS patients by using ML models (60%). SIGNIFICANCE: Thanks to our RS and ML methods, we were able to predict Parkinson’s disease (PD) progression in dissimilar groups of patients with different treatments. It might lead, in the future, to the discovery of universal rules of PD progression and optimise the treatment.