Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation最新文献

In this study, we fine-tuned a stable diffusion model to synthesize high resolution chest X-ray images (512×512) with bilateral lung edema caused by COVID-19 pneumonia using the class-specific prior preservation strategy. 300 positive images were selected from the MIDRC dataset as subject instances with an additional 400 negative images for class prior preservation. We synthesized images respectively using the new technique and the conventional technique for comparison. The synthetic images by the stable diffusion fine-tuned by the prior preservation technique have the Frechet inception distance (FID) of 9.2158 and kernel inception distance (KID) 0.0818 computed with the real positive images, which is superior to the synthetic images using the conventional methods such as WGAN and DDIM. The classification accuracy is 0.9975 with precision of 1.0 and recall of 0.9950 when the synthetic positive images with the real negative images were classified by a trained vision transformer (ViT). We conclude that the stable diffusion model can synthesize high-quality and high-resolution chest x-ray images using the prior preservation strategy with a small number of real images as subject instances and text prompt as guidance for the designated patterns.

Resting state functional Magnetic Resonance Imaging (rs-fMRI) is used to obtain spontaneous activation within the human brain in the absence of specific tasks. Analysis of the rs-fMRI data required spatially and functionally homogenous parcellation of the whole brain based on underlying temporal fluctuations. Commonly used parcellation schemes have a tradeoff between intra-cluster functional similarity and alignment with anatomical regions. In this article, we present a clustering scheme for rs-fMRI data that obtains spatially and functionally homogenous clusters. Results show that the proposed multistage approach can identify various brain networks. Moreover, the functional homogeneity of the clusters is shown to be better than those found with functional atlas and simple k-means clusters. The spatial homogeneity is shown to be better than Independent Component Analysis (ICA), and simple k-means clusters.

Cerebral Malaria (CM) is a severe neurological syndrome of malaria mainly found in children and is associated with highly specific retinal lesions. The manifestation of these indications of CM in the retina is called malarial retinopathy (MR). All patients showing clinical signs of CM are commonly diagnosed and treated accordingly; however, 23% of them are misdiagnosed as they suffer from another infection with identical clinical symptoms. Due to these underlying symptoms, the false positive cases may go untreated and could result in death of the patients. A diagnostic test is needed that is highly specific in order to reduce false positives. The purpose of this study to demonstrate a technique based on a transfer learning technique using images from three different retinal cameras to identify the hemorrhages and whitening lesions in the retina which can accurately identify the patients with MR. The MR detection model gives a specificity of 100% and a sensitivity of 90% with an AUC of 0.98. The algorithm demonstrates the potential of accurate MR detection with a low-cost retinal camera.

Accurate estimation of the apparent diffusion coefficient (ADC) of lesions in diffusion-weighted magnetic resonance imaging (DWMRI) is important to predict and monitor anti-cancer therapy response. The task of ADC estimation of lesions is complicated due to noise in the image, different variances in signal strengths at different b values and other random phenomena. In organs that have visceral motion, due to motion across scans, estimating the ADC becomes even more complex. To get rid of inaccuracies due to motion, only a single ADC value of the lesion is estimated, conventionally using a linear-regression (LR) approach. The LR approach is based on an inaccurate noise model and also suffers from other deficiencies. In this paper, we propose an easy-to-implement and computationally-fast maximum-likelihood (ML) method to estimate the ADC value of heterogeneous lesions in visceral organs. The proposed method takes into account the Rician distribution of noise in DWMRI. In the process, we also derive the statistical model for the measured mean signal intensity in DWMRI. We show using Monte-Carlo simulations that that the proposed method is more accurate than the LR method.

In diffusion-weighted magnetic resonance imaging, accurate segmentation of liver lesions in the diffusion-weighted images is required for computation of the apparent diffusion coefficient (ADC) of the lesion, the parameter that serves as an indicator of lesion response to therapy. However, the segmentation problem is challenging due to low SNR, fuzzy boundaries and speckle and motion artifacts. We propose a clustering algorithm that incorporates spatial information and a geometric constraint to solve this issue. We show that our algorithm provides improved accuracy compared to existing segmentation algorithms.