Medical image data is less accessible than in other domains due to privacy and regulatory constraints. In addition, labeling requires costly, time-intensive manual image annotation by clinical experts. To overcome these challenges, synthetic medical data generation offers a promising solution. Generative AI (GenAI), employing generative deep learning models, has proven effective at producing realistic synthetic images. This study proposes a novel mask-guided GenAI approach using diffusion models to generate synthetic fetal head ultrasound images paired with segmentation masks. These synthetic pairs augment real datasets for supervised fine-tuning of the Segment Anything Model (SAM). Our results show that the synthetic data captures real image features effectively, and this approach reaches state-of-the-art fetal head segmentation, especially when trained with a limited number of real image-mask pairs. In particular, the segmentation reaches Dice Scores of 94.66\% and 94.38\% using a handful of ultrasound images from the Spanish and African cohorts, respectively. Our code, models, and data are available on GitHub.

Congenital uterine anomalies (CUAs) can lead to infertility, miscarriage, preterm birth, and an increased risk of pregnancy complications. Compared to traditional 2D ultrasound (US), 3D US can reconstruct the coronal plane, providing a clear visualization of the uterine morphology for assessing CUAs accurately. In this paper, we propose an intelligent system for simultaneous automated plane localization and CUA diagnosis. Our highlights are: 1) we develop a denoising diffusion model with local (plane) and global (volume/text) guidance, using an adaptive weighting strategy to optimize attention allocation to different conditions; 2) we introduce a reinforcement learning-based framework with unsupervised rewards to extract the key slice summary from redundant sequences, fully integrating information across multiple planes to reduce learning difficulty; 3) we provide text-driven uncertainty modeling for coarse prediction, and leverage it to adjust the classification probability for overall performance improvement. Extensive experiments on a large 3D uterine US dataset show the efficacy of our method, in terms of plane localization and CUA diagnosis. Code is available at https://github.com/yuhoo0302/CUA-US.

Low-dose computed tomography (LDCT) reduces radiation exposure but suffers from image artifacts and loss of detail due to quantum and electronic noise, potentially impacting diagnostic accuracy. Transformer combined with diffusion models has been a promising approach for image generation. Nevertheless, existing methods exhibit limitations in preserving finegrained image details. To address this issue, frequency domain-directed diffusion transformer (FD-DiT) is proposed for LDCT reconstruction. FD-DiT centers on a diffusion strategy that progressively introduces noise until the distribution statistically aligns with that of LDCT data, followed by denoising processing. Furthermore, we employ a frequency decoupling technique to concentrate noise primarily in high-frequency domain, thereby facilitating effective capture of essential anatomical structures and fine details. A hybrid denoising network is then utilized to optimize the overall data reconstruction process. To enhance the capability in recognizing high-frequency noise, we incorporate sliding sparse local attention to leverage the sparsity and locality of shallow-layer information, propagating them via skip connections for improving feature representation. Finally, we propose a learnable dynamic fusion strategy for optimal component integration. Experimental results demonstrate that at identical dose levels, LDCT images reconstructed by FD-DiT exhibit superior noise and artifact suppression compared to state-of-the-art methods.

Weakly supervised semantic segmentation (WSSS) methods using class labels often rely on class activation maps (CAMs) to localize objects. However, traditional CAM-based methods struggle with partial activations and imprecise object boundaries due to optimization discrepancies between classification and segmentation. Recently, the conditional diffusion model (CDM) has been used as an alternative for generating segmentation masks in WSSS, leveraging its strong image generation capabilities tailored to specific class distributions. By modifying or perturbing the condition during diffusion sampling, the related objects can be highlighted in the generated images. Yet, the saliency maps generated by CDMs are prone to noise from background alterations during reverse diffusion. To alleviate the problem, we introduce Contrastive Learning with Diffusion Features (CLDF), a novel method that uses contrastive learning to train a pixel decoder to map the diffusion features from a frozen CDM to a low-dimensional embedding space for segmentation. Specifically, we integrate gradient maps generated from CDM external classifier with CAMs to identify foreground and background pixels with fewer false positives/negatives for contrastive learning, enabling robust pixel embedding learning. Experimental results on four segmentation tasks from two public medical datasets demonstrate that our method significantly outperforms existing baselines.

Cerebral infarction remains a leading cause of mortality and long-term disability globally, underscoring the critical importance of early diagnosis and timely intervention to enhance patient outcomes. This study introduces a novel approach to cerebral infarction segmentation using a novel dataset comprising MRI scans of 110 patients, retrospectively collected from Yozgat Bozok University Research Hospital. Two convolutional neural network architectures, the basic U-Net and the advanced U-Net3 + , are employed to segment infarction regions with high precision. Ground-truth annotations are generated under the supervision of an experienced radiologist, and data augmentation techniques are applied to address dataset limitations, resulting in 6732 balanced images for training, validation, and testing. Performance evaluation is conducted using metrics such as the dice score, Intersection over Union (IoU), pixel accuracy, and specificity. The basic U-Net achieved superior performance with a dice score of 0.8947, a mean IoU of 0.8798, a pixel accuracy of 0.9963, and a specificity of 0.9984, outperforming U-Net3 + despite its simpler architecture. U-Net3 + , with its complex structure and advanced features, delivered competitive results, highlighting the potential trade-off between model complexity and performance in medical imaging tasks. This study underscores the significance of leveraging deep learning for precise and efficient segmentation of cerebral infarction. The results demonstrate the capability of CNN-based architectures to support medical decision-making, offering a promising pathway for advancing stroke diagnosis and treatment planning.

This study aims to evaluate the success of artificial intelligence models developed using convolutional neural network-based algorithms on CBCT images. Labeling was done by segmentation method for 15 different conditions including caries, restorative filling material, root-canal filling material, dental implant, implant supported crown, crown, pontic, impacted tooth, supernumerary tooth, residual root, osteosclerotic area, periapical lesion, radiolucent jaw lesion, radiopaque jaw lesion, and mixed appearing jaw lesion on the data set consisting of 300 CBCT images. In model development, the Mask R-CNN architecture and ResNet 101 model were used as a transfer learning method. The success metrics of the model were calculated with the confusion matrix method. When the F1 scores of the developed models were evaluated, the most successful dental implant was found to be 1, and the lowest F1 score was found to be a mixed appearing jaw lesion. F1 scores were respectively dental implant, root canal filling material, implant supported crown, restorative filling material, radiopaque jaw lesion, crown, pontic, impacted tooth, caries, residual tooth root, radiolucent jaw lesion, osteosclerotic area, periapical lesion, supernumerary tooth, for mixed appearing jaw lesion; 1 is 0.99, 0.98, 0.98, 0.97, 0.96, 0.96, 0.95, 0.94, 0.94, 0.94, 0.90, 0.90, 0.87, and 0.8. Interpreting CBCT images is a time-consuming process and requires expertise. In the era of digital transformation, artificial intelligence-based systems that can automatically evaluate images and convert them into report format as a decision support mechanism will contribute to reducing the workload of physicians, thus increasing the time allocated to the interpretation of pathologies.

Accurate tumor delineation is foundational to radiotherapy. In the era of deep learning, the automation of this labor-intensive and variation-prone process is increasingly tractable. We developed a deep neural network model to segment gross tumor volumes (GTVs) in the lung and propagate them across 4D CT images to generate an internal target volume (ITV), capturing tumor motion during respiration. Using a multicenter cohort-based registry from 9 clinics across 2 health systems, we trained a 3D UNet model (iSeg) on pre-treatment CT images and corresponding GTV masks (n = 739, 5-fold cross-validation) and validated it on two independent cohorts (n = 161; n = 102). The internal cohort achieved a median Dice (DSC) of 0.73 [IQR: 0.62-0.80], with comparable performance in external cohorts (DSC = 0.70 [0.52-0.78] and 0.71 [0.59-79]), indicating multi-site validation. iSeg matched human inter-observer variability and was robust to image quality and tumor motion (DSC = 0.77 [0.68-0.86]). Machine-generated ITVs were significantly smaller than physician delineated contours (p < 0.0001), indicating more precise delineation. Notably, higher false positive voxel rate (regions segmented by the machine but not the human) were associated with increased local failure (HR: 1.01 per voxel, p = 0.03), suggesting the clinical relevance of these discordant regions. These results mark a leap in automated target volume segmentation and suggest that machine delineation can enhance the accuracy, reproducibility, and efficiency of this core task in radiotherapy.

To evaluate the efficacy of thin-slice T₂-weighted imaging (T₂WI) and super-resolution reconstruction (SRR) for preoperative assessment of vascular invasion in pancreatic ductal adenocarcinoma (PDAC). Ninety-five PDACs with preoperative MRI were retrospectively enrolled as a training set, with non-reconstructed T₂WI (NRT₂) in different slice thicknesses (NRT₂-3, 3 mm; NRT₂-5, ≥ 5 mm). A prospective test set was collected with NRT₂-5 (n = 125) only. A deep-learning network was employed to generate reconstructed super-resolution T₂WI (SRT₂) in different slice thicknesses (SRT₂-3, 3 mm; SRT₂-5, ≥ 5 mm). Image quality was assessed, including the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and signal-intensity ratio (SIR_t/p, tumor/pancreas; SIR_t/b, tumor/background). Diagnostic efficacy for vascular invasion was evaluated using the area under the curve (AUC) and compared across different slice thicknesses before and after reconstruction. SRT₂-5 demonstrated higher SNR and SIR_t/p compared to NRT₂-5 (74.18 vs 72.46; 1.42 vs 1.30; p < 0.05). SRT₂-3 showed increased SIR_t/p and SIR_t/b over NRT₂-3 (1.35 vs 1.31; 2.73 vs 2.58; p < 0.05). SRT₂-5 showed higher CNR, SIR_t/p and SIR_t/b than NRT₂-3 (p < 0.05). NRT₂-3 outperformed NRT₂-5 in evaluating venous invasion (AUC: 0.732 vs 0.597, p = 0.021). SRR improved venous assessment (AUC: NRT₂-3, 0.927 vs 0.732; NRT₂-5, 0.823 vs 0.597; p < 0.05), and SRT₂-5 exhibits comparable efficacy to NRT₂-3 in venous assessment (AUC: 0.823 vs 0.732, p = 0.162). Thin-slice T₂WI and SRR effectively improve the image quality and diagnostic efficacy for assessing venous invasion in PDAC. Thick-slice T₂WI with SRR is a potential alternative to thin-slice T₂WI. Both thin-slice T₂-WI and SRR effectively improve image quality and diagnostic performance, providing valuable options for optimizing preoperative vascular assessment in PDAC. Non-invasive and accurate assessment of vascular invasion supports treatment planning and avoids futile surgery. Vascular invasion evaluation is critical for the surgical eligibility of PDAC. SRR improved image quality and vascular assessment in T₂WI. Utilizing thin-slice T₂WI and SRR aids in clinical decision making for PDAC.

[Objective] In single-photon emission computed tomography (SPECT) image reconstruction, limited-angle conditions lead to a loss of frequency components, which distort the reconstructed tomographic image along directions corresponding to the non-collected projection angle range. Although conventional iterative image reconstruction methods have been used to improve the reconstructed images in limited-angle conditions, the image quality is still unsuitable for clinical use. We propose a limited-angle SPECT image reconstruction method that uses an end-to-end deep image prior (DIP) framework to improve reconstructed image quality.&#xD;[Approach] The proposed limited-angle SPECT image reconstruction is an end-to-end DIP framework which incorporates a forward projection model into the loss function to optimise the neural network. By also incorporating a binary mask that indicates whether each data point in the measured projection data has been collected, the proposed method restores the non-collected projection data and reconstructs a less distorted image.&#xD;[Main results] The proposed method was evaluated using 20 numerical phantoms and clinical patient data. In numerical simulations, the proposed method outperformed existing back-projection-based methods in terms of peak signal-to-noise ratio and structural similarity index measure. We analysed the reconstructed tomographic images in the frequency domain using an object-specific modulation transfer function, in simulations and on clinical patient data, to evaluate the response of the reconstruction method to different frequencies of the object. The proposed method significantly improved the response to almost all spatial frequencies, even in the non-collected projection angle range. The results demonstrate that the proposed method reconstructs a less distorted tomographic image.&#xD;[Significance] The proposed end-to-end DIP-based reconstruction method restores lost frequency components and mitigates image distortion under limited-angle conditions by incorporating a binary mask into the loss function.

Current virtual imaging phantoms primarily emphasize geometric&#xD;accuracy of anatomical structures. However, to enhance realism, it is also important&#xD;to incorporate intra-organ detail. Because biological tissues are heterogeneous in&#xD;composition, virtual phantoms should reflect this by including realistic intra-organ&#xD;texture and material variation.&#xD;We propose training two 3D Double U-Net conditional generative adversarial&#xD;networks (3D DUC-GAN) to generate sixteen unique textures that encompass organs&#xD;found within the torso. The model was trained on 378 CT image-segmentation&#xD;pairs taken from a publicly available dataset with 18 additional pairs reserved for&#xD;testing. Textured phantoms were generated and imaged using DukeSim, a virtual CT&#xD;simulation platform.&#xD;Results showed that the deep learning model was able to synthesize realistic&#xD;heterogeneous phantoms from a set of homogeneous phantoms. These phantoms were&#xD;compared with original CT scans and had a mean absolute difference of 46.15 ± 1.06&#xD;HU. The structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR)&#xD;were 0.86 ± 0.004 and 28.62 ± 0.14, respectively. The maximum mean discrepancy&#xD;between the generated and actual distribution was 0.0016. These metrics marked&#xD;an improvement of 27%, 5.9%, 6.2%, and 28% respectively, compared to current&#xD;homogeneous texture methods. The generated phantoms that underwent a virtual&#xD;CT scan had a closer visual resemblance to the true CT scan compared to the previous&#xD;method.&#xD;The resulting heterogeneous phantoms offer a significant step toward more realistic&#xD;in silico trials, enabling enhanced simulation of imaging procedures with greater fidelity&#xD;to true anatomical variation.