Medical image segmentation is crucial for many healthcare tasks, including disease diagnosis and treatment planning. One key area is the segmentation of skin lesions, which is vital for diagnosing skin cancer and monitoring patients. In this context, this paper introduces SegDT, a new segmentation model based on diffusion transformer (DiT). SegDT is designed to work on low-cost hardware and incorporates Rectified Flow, which improves the generation quality at reduced inference steps and maintains the flexibility of standard diffusion models. Our method is evaluated on three benchmarking datasets and compared against several existing works, achieving state-of-the-art results while maintaining fast inference speeds. This makes the proposed model appealing for real-world medical applications. This work advances the performance and capabilities of deep learning models in medical image analysis, enabling faster, more accurate diagnostic tools for healthcare professionals. The code is made publicly available at \href{https://github.com/Bekhouche/SegDT}{GitHub}.

Parameter-efficient fine-tuning (PEFT) of pre-trained foundation models is increasingly attracting interest in medical imaging due to its effectiveness and computational efficiency. Among these methods, Low-Rank Adaptation (LoRA) is a notable approach based on the assumption that the adaptation inherently occurs in a low-dimensional subspace. While it has shown good performance, its implementation requires a fixed and unalterable rank, which might be challenging to select given the unique complexities and requirements of each medical imaging downstream task. Inspired by advancements in natural image processing, we introduce a novel approach for medical image segmentation that dynamically adjusts the intrinsic rank during adaptation. Viewing the low-rank representation of the trainable weight matrices as a singular value decomposition, we introduce an l_1 sparsity regularizer to the loss function, and tackle it with a proximal optimizer. The regularizer could be viewed as a penalty on the decomposition rank. Hence, its minimization enables to find task-adapted ranks automatically. Our method is evaluated in a realistic few-shot fine-tuning setting, where we compare it first to the standard LoRA and then to several other PEFT methods across two distinguishable tasks: base organs and novel organs. Our extensive experiments demonstrate the significant performance improvements driven by our method, highlighting its efficiency and robustness against suboptimal rank initialization. Our code is publicly available: https://github.com/ghassenbaklouti/ARENA

Quantifiable image patterns associated with disease progression and treatment response are critical tools for guiding individual treatment, and for developing novel therapies. Here, we show that unsupervised machine learning can identify a pattern vocabulary of liver tissue in magnetic resonance images that quantifies treatment response in diffuse liver disease. Deep clustering networks simultaneously encode and cluster patches of medical images into a low-dimensional latent space to establish a tissue vocabulary. The resulting tissue types capture differential tissue change and its location in the liver associated with treatment response. We demonstrate the utility of the vocabulary in a randomized controlled trial cohort of patients with nonalcoholic steatohepatitis. First, we use the vocabulary to compare longitudinal liver change in a placebo and a treatment cohort. Results show that the method identifies specific liver tissue change pathways associated with treatment and enables a better separation between treatment groups than established non-imaging measures. Moreover, we show that the vocabulary can predict biopsy derived features from non-invasive imaging data. We validate the method in a separate replication cohort to demonstrate the applicability of the proposed method.

Detecting malignant transformation of sinonasal inverted papilloma (SIP) into squamous cell carcinoma (SIP-SCC) before surgery is a clinical need. We aimed to explore the value of deep learning (DL) that leverages nasal endoscopy and T2-weighted magnetic resonance imaging (T2W-MRI) for automated tumor segmentation and differentiation between SIP and SIP-SCC. We conducted a retrospective analysis of 174 patients diagnosed with SIPs, who were divided into a training cohort (n = 121) and a testing cohort (n = 53). Three DL architectures were utilized to train automated segmentation models for endoscopic and T2W-MRI images. DL scores predicting SIP-SCC were generated using DenseNet121 from both modalities and combined to create a dual-modality DL nomogram. The diagnostic performance of the DL models was assessed alongside two radiologists, evaluated through the area under the receiver operating characteristic curve (AUROC), with comparisons made using the Delong method. In the testing cohort, the FCN_ResNet101 and VNet exhibited superior performance in automated segmentation, achieving mean dice similarity coefficients of 0.95 ± 0.03 for endoscopy and 0.93 ± 0.02 for T2W-MRI, respectively. The dual-modality DL nomogram based on automated segmentation demonstrated the highest predictive performance for SIP-SCC (AUROC 0.865), outperforming the radiology resident (AUROC 0.672, p = 0.071) and the attending radiologist (AUROC 0.707, p = 0.066), with a trend toward significance. Notably, both radiologists improved their diagnostic performance with the assistance of the DL nomogram (AUROCs 0.734 and 0.834). The DL framework integrating endoscopy and T2W-MRI offers a fully automated predictive tool for SIP-SCC. The integration of endoscopy and T2W-MRI within a well-established DL framework enables fully automated prediction of SIP-SSC, potentially improving decision-making for patients with suspicious SIP. Detecting the transformation of SIP into SIP-SCC before surgery is both critical and challenging. Endoscopy and T2W-MRI were integrated using DL for predicting SIP-SCC. The dual-modality DL nomogram outperformed two radiologists. The nomogram may improve decision-making for patients with suspicious SIP.

To construct an artificial intelligence (AI)-assisted model for identifying the infraorbital posterior ethmoid cells (IPECs) based on deep learning using sagittal CT images. Sagittal CT images of 277 samples with and 142 samples without IPECs were retrospectively collected. An experienced radiologist engaged in the relevant aspects picked a sagittal CT image that best showed IPECs. The images were randomly assigned to the training and test sets, with 541 sides in the training set and 97 sides in the test set. The training set was used to perform a five-fold cross-validation, and the results of each fold were used to predict the test set. The model was built using nnUNet, and its performance was evaluated using Dice and standard classification metrics. The model achieved a Dice coefficient of 0.900 in the training set and 0.891 in the additional set. Precision was 0.965 for the training set and 1.000 for the additional set, while sensitivity was 0.981 and 0.967, respectively. A comparison of the diagnostic efficacy between manual outlining by a less-experienced radiologist and AI-assisted outlining showed a significant improvement in detection efficiency (P < 0.05). The AI model aided correctly in identifying and outlining all IPECs, including 12 sides that the radiologist should improve portraying. AI models can help radiologists identify the IPECs, which can further prompt relevant clinical interventions.

The specific role of lysophospholipids (LysoPLs) in the pathogenesis of chronic obstructive pulmonary disease (COPD) is not yet fully understood. We determined serum LysoPLs in 20 patients with stable COPD and 20 healthy smokers using liquid chromatography-mass spectrometry (LC-MS) and matching with the lipidIMMS library, and integrated these data with spirometry, systemic inflammation markers, and quantitative chest CT generated by an automated 3D-U-Net artificial intelligence algorithm model. Our findings identified three differential LysoPLs, lysophosphatidylcholine (LPC) (18:0), LPC (18:1), and LPC (18:2), which were significantly lower in the COPD group than in healthy smokers. Significant negative correlations were observed between these LPCs and the inflammatory markers C-reactive protein and Interleukin-6. LPC (18:0) and (18:2) correlated with higher post-bronchodilator FEV1, and the latter also correlated with FEV1% predicted, forced vital capacity (FVC), and FEV1/FVC ratio. Additionally, these three LPCs were negatively correlated with the volume and percentage of low attenuation areas (LAA), high-attenuation areas (HAA), honeycombing, reticular patterns, ground-glass opacities (GGO), and consolidation on CT imaging. In the patients with COPD, the three LPCs were most significantly associated with HAA and GGO. In conclusion, patients with stable COPD exhibited a unique LysoPL metabolism profile, with LPC (18:0), LPC (18:1), and LPC (18:2) being the most significantly altered lipid molecules. The reduction in these three LPCs was associated with impaired pulmonary function and were also linked to a greater extent of emphysema and interstitial lung abnormalities.

Limited-angle computed tomography (LACT) offers patient-friendly benefits, such as rapid scanning and reduced radiation exposure. However, the incompleteness of data in LACT often causes notable artifacts, posing challenges for precise medical interpretation. Although numerous approaches have been introduced to reconstruct LACT images into complete computed tomography (CT) scans, they focus on improving image quality and operate separately from lesion segmentation models, often overlooking essential lesion-specific information. This is because reconstruction models are primarily optimized to satisfy overall image quality rather than local lesion-specific regions, in a non-end-to-end setup where each component is optimized independently and may not contribute to reaching the global minimum of the overall objective function. To address this problem, we propose LA-Seg, a transformer-based segmentation model using the sinogram domain of LACT data. The LA-Seg method uses an auxiliary reconstruction task to estimates incomplete sinogram regions to enhance segmentation robustness. Applying transformers adapted from video prediction models captures the spatial structure and sequential patterns in sinograms and reconstructs features in incomplete regions using a disentangled representation guided by distinctive patterns. We propose contrastive abnormal feature loss to distinguish between normal and abnormal regions better. The experimental results demonstrate that LA-Seg consistently surpasses existing medical segmentation approaches in diverse LACT conditions. The source code is provided at https://github.com/jhyoon964/LA-Seg.

Medical image segmentation suffers from data scarcity, particularly in polyp detection where annotation requires specialized expertise. We present SynDiff, a framework combining text-guided synthetic data generation with efficient diffusion-based segmentation. Our approach employs latent diffusion models to generate clinically realistic synthetic polyps through text-conditioned inpainting, augmenting limited training data with semantically diverse samples. Unlike traditional diffusion methods requiring iterative denoising, we introduce direct latent estimation enabling single-step inference with T x computational speedup. On CVC-ClinicDB, SynDiff achieves 96.0% Dice and 92.9% IoU while maintaining real-time capability suitable for clinical deployment. The framework demonstrates that controlled synthetic augmentation improves segmentation robustness without distribution shift. SynDiff bridges the gap between data-hungry deep learning models and clinical constraints, offering an efficient solution for deployment in resourcelimited medical settings.

To investigate lumbar vertebral volumetric bone mineral density (vBMD) from ex vivo opportunistic multi-detector computed tomography (MDCT) scans using different protocols, and compare it to dedicated quantitative CT (QCT) values from the same specimens. Cadavers from two female donors (ages 62 and 68 years) were scanned (L1-L5) using six different MDCT protocols and one dedicated QCT scan. Opportunistic vBMD was extracted using an artificial intelligence-based algorithm. The vBMD measurements from the six MDCT protocols, which varied in peak tube voltage (80-140 kVp), tube load (72-200 mAs), slice thickness (0.75-1 mm), and/or slice increment (0.5-0.75 mm), were compared to those obtained from dedicated QCT. A strong positive correlation was observed between vBMD from opportunistic MDCT and reference QCT (ρ = 0.869, p < 0.01). Agreement between vBMD measurements from MDCT protocols and the QCT reference standard according to the intraclass correlation coefficient (ICC) was 0.992 (95% confidence interval [CI]: 0.982-0.998). Bland-Altman analysis showed biases ranging from - 12.66 to 8.00 mg/cm³ across the six MDCT protocols, with all data points falling within the respective limits of agreement (LOA) for both cadavers. Opportunistic vBMD measurements of lumbar vertebrae demonstrated reliable consistency ex vivo across various scan parameters when compared to dedicated QCT.

The aim of this study was to evaluate the benefit of a volumetric AI-based body composition analysis (BCA) algorithm in multiple myeloma (MM). Therefore, a retrospective monocentric cohort of 91 MM patients was analyzed. The BCA algorithm, powered by a convolutional neural network, quantified tissue compartments and bone density based on routine CT scans. Correlations between BCA data and demographic/clinical parameters were investigated. BCA-endotypes were identified and survival rates were compared between BCA-derived patient clusters. Patients with high-risk cytogenetics exhibited elevated cardiac marker index values. Across Revised-International Staging System (R-ISS) categories, BCA parameters did not show significant differences. However, both subcutaneous and total adipose tissue volumes were significantly lower in patients with progressive disease or death during follow-up compared to patients without progression. Cluster analysis revealed two distinct BCA-endotypes, with one group displaying significantly better survival. Furthermore, a combined model composed of clinical parameters and BCA data demonstrated a higher predictive capability for disease progression compared to models based solely on high-risk cytogenetics or R-ISS. These findings underscore the potential of BCA to improve patient stratification and refining prognostic models in MM.