Space vehicles and habitats are complex systems with tightly coupled interdependencies. They operate under extreme, unforgiving conditions that challenge system performance and crew safety. Effective health management in such systems demands strong architectural and situational knowledge, leading to heavy reliance on ground control. As missions venture deeper into space, growing communication delays make such support impractical, prompting a paradigm shift toward greater on-board autonomy. However, computational constraints limit the migration of all health management components to the habitat. Running high-fidelity fault detection and diagnosis modules on the ground can conserve on-board resources but introduces delays in decision-making. This latency increases the need for effective decision-making policies, which may require interrupting agents to enable timely responses to higher-priority situations. In this paper, we study how communication delays and agent interruptibility impact decision-making in a habitat system, using the Human-centered Autonomous Resilient Space Habitat (HARSH), a reconfigurable cyber-physical testbed. A series of experiments is conducted to evaluate different design configurations under single and compound disruption scenarios. The ability of each configuration to respond and recover from faults is assessed using resilience metrics. Findings from the experiments inform recommendations to advance autonomous decision-making policies and enhance system resilience in future deep space habitats.

Medical imaging datasets often exhibit heterogeneity from variations in acquisition sites, scanner, and patient demographics, leading to domain shifts that undermine the generalization of AI models. Existing approaches-such as image harmonization, domain adaptation, and domain generalization-typically rely on labeled data (e.g., segmentation masks) or explicit domain metadata (e.g., demographic attributes), which are costly and often unavailable. In this study, we propose Mixture normalization for Unified representation (MixUnify), which harmonizes pixel-level heterogeneity in input images without supervision. MixUnify models the global data distribution via a Gaussian Mixture Model (GMM), under the assumption that each domain follows a local gaussian distribution. The estimated GMM parameters are then used for Mixture Normalization. Notably, the GMM parameters are optimized through a Gaussian Matching loss that leverages feature statistics extracted from the images. We evaluate our method on three public datasets-prostate MRI, cardiac MRI, and retinal fundus images-and demonstrate that MixUnify significantly enhances segmentation performance across diverse domains. Our code is available at https://github.com/Gyeongdeok-Jo/MixUnify.

Due to privacy concerns, obtaining large datasets is challenging in medical image analysis, especially with 3D modalities like Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Existing generative models, developed to address this issue, often face limitations in output diversity and thus cannot accurately represent 3D medical images. We propose a tumor-generation model that utilizes radiomics features as generative conditions. Radiomics features are high-dimensional handcrafted semantic features that are biologically well-grounded and thus are good candidates for conditioning. Our model employs a GAN-based model to generate tumor masks and a diffusion-based approach to generate tumor texture conditioned on radiomics features. Our method allows the user to generate tumor images according to user-specified radiomics features such as size, shape, and texture at an arbitrary location. This enables the physicians to easily visualize tumor images to better understand tumors according to changing radiomics features. Our approach allows for the removal, manipulation, and repositioning of tumors, generating various tumor types in different scenarios. The model has been tested on tumors in four different organs (kidney, lung, breast, and brain) across CT and MRI. The synthesized images are shown to effectively aid in training for downstream tasks and their authenticity was also evaluated through expert evaluations. Our method has potential usage in treatment planning with diverse synthesized tumors. Our code is available at github.com/jongdoryITS-Radiomics.

White matter hyperintensities (WMHs) are critical indicators of cerebral small vessel disease and are linked to stroke and cognitive decline. Accurate WHM segmentation is challenging due to limited contrast and small size, particularly for deep WMHs where T1-weighted and FLAIR MRI are routinely used. Image fusion offers a method to combine two modalities into one fused modality useful for WHM segmentation. We propose DeAFusion: Detail-Aware Image Fusion, a novel image fusion framework. This framework introduces a Pixel-wise Information Preservation Degree map to enable detailed control of fusion at the pixel level and incorporates segmentation loss to embed semantic information. Our model outperforms existing fusion methods in enhancing lesion visibility and preserving structural details, as demonstrated on both public and in-house datasets. DeAFusion shows promise for improving clinical assessments of WMHs. Our code is available at https://github.com/Gyeongdeok-Jo/DeAFusion.

Accurate segmentation of cerebral microbleeds (CMBs) is important for diagnosing small vessel diseases, yet it presents significant challenges due to their tiny size and especially the high risk of false positives (e.g., calcifications and blood flow in pial vessels) in magnetic resonance imaging (MRI). While existing studies have employed multi-stage deep neural networks to reduce false positives, these approaches often rely heavily on the performance of the false positive reduction module. To address this and move towards an end-to-end learning scheme, we propose a novel approach that incorporates self-supervised learning through masked image modeling to enrich both encoder and decoder features in UNETR++ using gradient recalled echo T2*-weighted MRI. Noting that existing pre-training strategies often focus on the encoder while neglecting the decoder, our approach pre-trains both components by introducing segmentation and reconstruction heads. We evaluated our model on an in-house dataset and external validation set, demonstrating superior performance compared to the state-of-the-art nnUNet and nnDetection. Additionally, ablation studies revealed that pre-training both the encoder and decoder subsequently benefits the overall performance of our framework. Our code is available at https://github.com/junmokwon/UNETRppCMBSeg.

Multi-modal images play a crucial role in comprehensive evaluations in medical image analysis providing complementary information for identifying clinically important biomarkers. However, in clinical practice, acquiring multiple modalities can be challenging due to reasons such as scan cost, limited scan time, and safety considerations. In this paper, we propose a model based on the latent diffusion model (LDM) that leverages switchable blocks for image-to-image translation in 3D medical images without patch cropping. The 3D LDM combined with conditioning using the target modality allows generating high-quality target modality in 3D overcoming the shortcoming of the missing out-of-slice information in 2D generation methods. The switchable block, noted as multiple switchable spatially adaptive normalization (MS-SPADE), dynamically transforms source latents to the desired style of the target latents to help with the diffusion process. The MS-SPADE block allows us to have one single model to tackle many translation tasks of one source modality to various targets removing the need for many translation models for different scenarios. Our model exhibited successful image synthesis across different source-target modality scenarios and surpassed other models in quantitative evaluations tested on multi-modal brain magnetic resonance imaging datasets of four different modalities and an independent IXI dataset. Our model demonstrated successful image synthesis across various modalities even allowing for one-to-many modality translations. Furthermore, it outperformed other one-to-one translation models in quantitative evaluations. Our code is available at https://github.com/jongdory/ALDM/

This study investigated the impact of task types (functional vs. social) and the gendered voices (female vs. male) of Siri, an intelligent virtual assistant (IVA), on social presence and trust perceptions toward the IVA.In an online experiment involving 172 participants, individuals were randomly assigned to one of four conditions, interacting with Siri on their iPhones for various task inquiries.Results from multivariate analyses of covariances revealed significant differences in trust levels based on the type of task.Trust was found to be higher for functional tasks when assisted by Siri, compared to social tasks.However, there was no significant difference in trust based on Siri's gendered voices.Post-hoc analyses indicated significant interactions between the gender match of participants and Siri's gendered voices in two dimensions of trust.Men tended to trust the male-voiced Siri more than the femalevoiced Siri, while women did not exhibit a preference for the female voice over the male voice.This study successfully replicated the task effect observed in prior research but did not replicate the gender effect.Key distinctions between the current study and previous ones include language and the participants' nationality, with this study focusing on Korean participants interacting with Korean Siri.

The prognosis, hence survival, of patients with brain tumors is highly dependent on the size and grade of the tumor. Thus, joint learning of brain tumor segmentation and overall survival of patients with brain tumors can benefit each other. In this work, we explored the feasibility of prediction of patients' overall survival through U-Net guided by the information of brain tumor segmentation. We evaluated the proposed model on the multimodal brain tumor segmentation (BraTS) 2017 challenge dataset. We achieved the mean Dice score of 0.595 for brain tumor segmentation on the test set. The Pearson correlation coefficient for overall survival prediction on the test set was 0.243, indicating promising results for both brain tumor segmentation and overall survival prediction.

Spectral-domain optical coherence tomography (SD-OCT) images inevitably suffer from multiplicative speckle noise caused by random interference. This study proposes an unsupervised domain adaptation approach for noise reduction by translating the SD-OCT to the corresponding high-quality enhanced depth imaging (EDI)-OCT. We propose a structure-persevered cycle-consistent generative adversarial network for unpaired image-to-image translation, which can be applied to imbalanced unpaired data, and can effectively preserve retinal details based on a structure-specific cross-domain description. It also imposes smoothness by penalizing the intensity variation of the low reflective region between consecutive slices. Our approach was tested on a local data set that consisted of 268 SD-OCT volumes and two public independent validation datasets including 20 SD-OCT volumes and 17 B-scans, respectively. Experimental results show that our method can effectively suppress noise and maintain the retinal structure, compared with other traditional approaches and deep learning methods in terms of qualitative and quantitative assessments. Our proposed method shows good performance for speckle noise reduction and can assist downstream tasks of OCT analysis.

Imaging genetics is a method used to detect associations between imaging and genetic variables. Some researchers have used sparse canonical correlation analysis (SCCA) for imaging genetics. This study was conducted to improve the efficiency and interpretability of SCCA. We propose a connectivity-based penalty for incorporating biological prior information. Our proposed approach, named joint connectivity-based SCCA (JCB-SCCA), includes the proposed penalty and can handle multi-modal neuroimaging datasets. Different neuroimaging techniques provide distinct information on the brain and have been used to investigate various neurological disorders, including Parkinson's disease (PD). We applied our algorithm to simulated and real imaging genetics datasets for performance evaluation. Our algorithm was able to select important features in a more robust manner compared with other multivariate methods. The algorithm revealed promising features of single-nucleotide polymorphisms and brain regions related to PD by using a real imaging genetic dataset. The proposed imaging genetics model can be used to improve clinical diagnosis in the form of novel potential biomarkers. We hope to apply our algorithm to cohorts such as Alzheimer's patients or healthy subjects to determine the generalizability of our algorithm.

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. The automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their methods on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge. Sixty T1 + FLAIR images from three MR scanners were released with the manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. The segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: 1) Dice similarity coefficient; 2) modified Hausdorff distance (95th percentile); 3) absolute log-transformed volume difference; 4) sensitivity for detecting individual lesions; and 5) F1-score for individual lesions. In addition, the methods were ranked on their inter-scanner robustness; 20 participants submitted their methods for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation.

High-throughput RNA-seq technology has emerged as a powerful tool for understanding the molecular basis of phenotype variation in biology, including disease. Recently, some correlated RNA-seq datasets started to be generated. While there have been several approaches proposed for identifying the differentially expressed genes (DEGs), not many methods can analyze correlated RNA-seq data. We expect the simultaneous analysis of correlated RNA-seq data to increase of power of detecting DEGs. We propose a multivariate method to find DEGs on correlated RNA-seq data based on the Generalized Estimating Equations (GEE) approach. The advantage of the proposed method is to consider correlated RNA-seq data simultaneously while accounting for correlations. Through real data analysis and simulation studies, we show that our multivariate approach has higher power of detecting DEGs than the existing methods.

MOTIVATION: It is expected that individual genes have intrinsically different variability in the global expressional trend among them. Thus, the consideration of gene-specific expressional properties will help us to distinguish target-selective gene expression over non-selective over-expression. RESULTS: The re-standardization and integration of heterogeneous microarray datasets, available from public databases, have enabled us to determine the global expression properties of individual genes across a wide variety of experimental conditions and samples. The global averages and SDs of expression for each gene in the integrated microarray datasets were found to be intrinsic properties, which were consistent among independent collections of datasets using different microarray platforms. Using the gene-specific intrinsic parameters to rescale the microarray data, we were able to distinguish novel selective gene expression [cartilage oligomeric matrix protein (COMP) and Collagen X] in breast cancer tissues from non-selective over-expression, a difference that has not been detectable by conventional methods. AVAILABILITY AND IMPLEMENTATION: The web-based tool for GS-LAGE is available at http://lage.sookmyung.ac.kr

We propose a method of using a relatively low degree of freedom (DOF) warping to accurately measure the interval change of lesions having homogeneous contrast. The setting presented here presupposes the use of interval computed tomography (CT) liver exams. After a 3 x 24 DOF warping of the later examination to match the liver's pose in the earlier exam of the interval pair is performed, the lesion's volume change is estimated using the computed difference volume of the two data sets via a novel method that counts partial volume contributions and is insensitive to slight misregistration. A mathematically generated phantom is used to quantify accuracy in the presence of noise. We also quantify the accuracy of our CT liver registrations using microcoils implanted for chemotherapy. A probabilistic liver atlas is used to support automatic masking and liver-focused registration.

There have been significant efforts to build a probabilistic atlas of the brain and to use it for many common applications, such as segmentation and registration. Though the work related to brain atlases can be applied to nonbrain organs, less attention has been paid to actually building an atlas for organs other than the brain. Motivated by the automatic identification of normal organs for applications in radiation therapy treatment planning, we present a method to construct a probabilistic atlas of an abdomen consisting of four organs (i.e., liver, kidneys, and spinal cord). Using 32 noncontrast abdominal computed tomography (CT) scans, 31 were mapped onto one individual scan using thin plate spline as the warping transform and mutual information (MI) as the similarity measure. Except for an initial coarse placement of four control points by the operators, the MI-based registration was automatic. Additionally, the four organs in each of the 32 CT data sets were manually segmented. The manual segmentations were warped onto the "standard" patient space using the same transform computed from their gray scale CT data set and a probabilistic atlas was calculated. Then, the atlas was used to aid the segmentation of low-contrast organs in an additional 20 CT data sets not included in the atlas. By incorporating the atlas information into the Bayesian framework, segmentation results clearly showed improvements over a standard unsupervised segmentation method.