Disease Progression Timeline Estimation for Alzheimer's Disease using Discriminative Event Based Modeling

Alzheimer's Disease (AD) is characterized by a cascade of biomarkers becoming abnormal, the pathophysiology of which is very complex and largely unknown. Event-based modeling (EBM) is a data-driven technique to estimate the sequence in which biomarkers for a disease become abnormal based on cross-sectional data. It can help in understanding the dynamics of disease progression and facilitate early diagnosis and prognosis. In this work we propose a novel discriminative approach to EBM, which is shown to be more accurate than existing state-of-the-art EBM methods. The method first estimates for each subject an approximate ordering of events. Subsequently, the central ordering over all subjects is estimated by fitting a generalized Mallows model to these approximate subject-specific orderings. We also introduce the concept of relative distance between events which helps in creating a disease progression timeline. Subsequently, we propose a method to stage subjects by placing them on the estimated disease progression timeline. We evaluated the proposed method on Alzheimer's Disease Neuroimaging Initiative (ADNI) data and compared the results with existing state-of-the-art EBM methods. We also performed extensive experiments on synthetic data simulating the progression of Alzheimer's disease. The event orderings obtained on ADNI data seem plausible and are in agreement with the current understanding of progression of AD. The proposed patient staging algorithm performed consistently better than that of state-of-the-art EBM methods. Event orderings obtained in simulation experiments were more accurate than those of other EBM methods and the estimated disease progression timeline was observed to correlate with the timeline of actual disease progression. The results of these experiments are encouraging and suggest that discriminative EBM is a promising approach to disease progression modeling

Learning unbiased group-wise registration (LUGR) and joint segmentation: evaluation on longitudinal diffusion MRI

Analysis of longitudinal changes in imaging studies often involves both segmentation of structures of interest and registration of multiple timeframes. The accuracy of such analysis could benefit from a tailored framework that jointly optimizes both tasks to fully exploit the information available in the longitudinal data. Most learning-based registration algorithms, including joint optimization approaches, currently suffer from bias due to selection of a fixed reference frame and only support pairwise transformations. We here propose an analytical framework based on an unbiased learning strategy for group-wise registration that simultaneously registers images to the mean space of a group to obtain consistent segmentations. We evaluate the proposed method on longitudinal analysis of a white matter tract in a brain MRI dataset with 2-3 time-points for 3249 individuals, i.e., 8045 images in total. The reproducibility of the method is evaluated on test-retest data from 97 individuals. The results confirm that the implicit reference image is an average of the input image. In addition, the proposed framework leads to consistent segmentations and significantly lower processing bias than that of a pair-wise fixed-reference approach. This processing bias is even smaller than those obtained when translating segmentations by only one voxel, which can be attributed to subtle numerical instabilities and interpolation. Therefore, we postulate that the proposed mean-space learning strategy could be widely applied to learning-based registration tasks. In addition, this group-wise framework introduces a novel way for learning-based longitudinal studies by direct construction of an unbiased within-subject template and allowing reliable and efficient analysis of spatio-temporal imaging biomarkers.Comment: SPIE Medical Imaging 2021 (oral

Intrasubject multimodal groupwise registration with the conditional template entropy

Image registration is an important task in medical image analysis. Whereas most methods are designed for the registration of two images (pairwise registration), there is an increasing interest in simultaneously aligning more than two images using groupwise registration. Multimodal registration in a groupwise setting remains difficult, due to the lack of generally applicable similarity metrics. In this work, a novel similarity metric for such groupwise registration problems is proposed. The metric calculates the sum of the conditional entropy between each image in the group and a representative template image constructed iteratively using principal component analysis. The proposed metric is validated in extensive experiments on synthetic and intrasubject clinical image data. These experiments showed equivalent or improved registration accuracy compared to other state-of-the-art (dis)similarity metrics and improved transformation consistency compared to pairwise mutual information

STRATEGI PENGEMBANGAN USAHA KECAP (STUDI KASUS PERUSAHAAN KECAP MIROSO DI KABUPATEN KLATEN)

Abstrak : Perusahaan Kecap Miroso adalah salah satu perusahaan kecap yang berada di Kabupaten Klaten yang masih memiliki potensi untuk dikembangkan. Penelitian ini bertujuan untuk mengidentifikasi kekuatan, kelemahan, peluang dan ancaman; merumuskan alternatif strategi; dan prioritas strategi untuk Perusahaan Kecap Miroso. Metode dasar yang digunakan adalah deskriptif analitik dengan teknik studi kasus. Alat analisis yang digunakan adalah Matriks IFE, Matriks EFE, Matriks SWOT dan Matriks QSP. Hasil penelitian menunjukkan bahwa Perusahaan Kecap Miroso berada di kuadran I (Progresif). Alternatif strategi pengembangan untuk Perusahaan Kecap Miroso meliputi: Peningkatan mutu SDM melalui pelatihan guna menjaga kualitas produk, Diversifikasi kemasan produk kecap ke kemasan sachet guna menambah segmen pasar baru pada konsumen rumah tangga, Menetapkan SOP (Standar Operasional Prosedur) dalam menjalankan proses produksi guna menjaga kualitas kecap yang dihasilkan, Pemasaran kecap ke wilayah yang baru guna meningkatan kuantitas penjualan yang berdampak pada peningkatan omset perusahaan. Prioritas strategi berdasarkan Matriks QSP diperoleh skor 5,9635 adalah Diversifikasi kemasan produk kecap ke kemasan sachet guna menambah segmen pasar baru pada konsumen rumah tangga

Evaluation of semiautomated internal carotid artery stenosis quantification from 3-dimensional contrast-enhanced magnetic resonance angiograms

Rationale and Objectives: The performance of a semiautomatic technique for internal carotid artery (ICA) stenosis quantification of the internal carotid artery in contrast-enhanced magnetic resonance angiography was evaluated. Materials and Methods: The degree of stenosis of 52 ICAs was quantified by measuring the cross-sectional area along the center lumen line. This was performed both by 3 independent observers and the semiautomated method. The degree of stenosis was defined as the amount of cross-sectional lumen reduction. Results: Agreement between the method and observers was good (weighted-kappa, kappa(w) = 0.89). Reproducibility of measurements of the semiautomated technique was better (kappa(w) = 0.97) than that of the observers (kappa(w) = 0.76), and the evaluated technique was considerably less time-consuming. Conclusions: Because the user interaction is limited, this technique can be used to replace an expert observer in 3-dimensional stenosis quantification of the ICA at CE-MRA in clinical practice