Accurate segmentation of organs from abdominal CT scans is essential for clinical applications such as diagnosis, treatment planning, and patient monitoring. To handle challenges of heterogeneity in organ shapes, sizes, and complex anatomical relationships, we propose a Multi decoder network (MDNet), an encoder-decoder network that uses the pre-trained MiT-B2 as the encoder and multiple different decoder networks. Each decoder network is connected to a different part of the encoder via a multi-scale feature enhancement dilated block. With each decoder, we increase the depth of the network iteratively and refine segmentation masks, enriching feature maps by integrating previous decoders’ feature maps. To refine the feature map further, we also utilize the predicted masks from the previous decoder to the current decoder to provide spatial attention across foreground and background regions. MDNet effectively refines the segmentation mask with a high dice similarity coefficient (DSC) of 0.9013 and 0.9169 on the Liver Tumor segmentation (LiTS) and MSD Spleen datasets. Additionally, it reduces Hausdorff distance (HD) to 3.79 for the LiTS dataset and 2.26 for the spleen segmentation dataset, underscoring the precision of MDNet in capturing the complex contours. Moreover, MDNet is more interpretable and robust compared to the other baseline models. The code for our architecture is available at https://github.com/DebeshJha/MDNet.

Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective study, we collected a large dataset (767 scans from 499 participants) of T1-weighted (T1 W) and T2-weighted (T2 W) abdominal MRI series from five centers between March 2004 and November 2022. We also collected CT scans of 1,350 patients from publicly available sources for benchmarking purposes. We introduced a new pancreas segmentation method, called PanSegNet , combining the strengths of nnUNet and a Transformer network with a new linear attention module enabling volumetric computation. We tested PanSegNet ’s accuracy in cross-modality (a total of 2,117 scans) and cross-center settings with Dice and Hausdorff distance (HD95) evaluation metrics. We used Cohen’s kappa statistics for intra and inter-rater agreement evaluation and paired t-tests for volume and Dice comparisons, respectively. For segmentation accuracy, we achieved Dice coefficients of 88.3% (±7.2%, at case level) with CT, 85.0% (±7.9%) with T1 W MRI, and 86.3% (±6.4%) with T2 W MRI. There was a high correlation for pancreas volume prediction with R 2 of 0.91, 0.84, and 0.85 for CT, T1 W, and T2 W, respectively. We found moderate inter-observer (0.624 and 0.638 for T1 W and T2 W MRI, respectively) and high intra-observer agreement scores. All MRI data is made available at https://osf.io/kysnj/ . Our source code is available at https://github.com/NUBagciLab/PaNSegNet . • We develop a first-ever cross-platform compatible (T1 W, T2 W, and CT) pancreas segmentation tool, named PanSegNet . • PaNSegNet has innovative “linear self-attention” blocks to reduce computational cost significantly while operating on 3D. • We shared our both source code and multi-center multi-contrast MRI datasets with ground truths. • PaNSegNet underwent rigorous validation, including cross-domain and multi-center comparisons between CT and MRI scans.

Large-scale, big-variant, high-quality data are crucial for developing robust and successful deep-learning models for medical applications since they potentially enable better generalization performance and avoid overfitting. However, the scarcity of high-quality labeled data always presents significant challenges. This paper proposes a novel approach to address this challenge by developing controllable diffusion models for medical image synthesis, called DiffBoost. We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data that preserve the essential characteristics of the original medical images by incorporating edge information of objects to guide the synthesis process. In our approach, we ensure that the synthesized samples adhere to medically relevant constraints and preserve the underlying structure of imaging data. Due to the random sampling process by the diffusion model, we can generate an arbitrary number of synthetic images with diverse appearances. To validate the effectiveness of our proposed method, we conduct an extensive set of medical image segmentation experiments on multiple datasets, including Ultrasound breast (+13.87%), CT spleen (+0.38%), and MRI prostate (+7.78%), achieving significant improvements over the baseline segmentation methods. The promising results demonstrate the effectiveness of our DiffBoost for medical image segmentation tasks and show the feasibility of introducing a first-ever text-guided diffusion model for general medical image segmentation tasks. With carefully designed ablation experiments, we investigate the influence of various data augmentations, hyper-parameter settings, patch size for generating random merging mask settings, and combined influence with different network architectures. Source code with checkpoints are available at https://github.com/NUBagciLab/DiffBoost.

The interaction between deep brain stimulation (DBS) systems and magnetic resonance imaging (MRI) can induce tissue heating in patients. While electromagnetic (EM) simulations can be used to estimate the specific absorption rate (SAR) values in the presence of an implanted DBS system, they are computationally expensive. To address this drawback, we predict local SAR values in the tips of DBS leads with machine learning based efficient algorithms, specifically XgBoost and deep learning. We significantly outperformed the previous state of the art, and adapted new machine learning models based on Residual Networks family as well as XgBoost models. We observed that already extracted limited features are better suited for ensemble learning via XgBoost than deep networks due the small-data regime. Although we conclude that boosting gradient algorithm is more suitable for this non-linear regression problem due to structured nature of the data and small data regime, we found that width plays a more critical role than depth in network design and it has a strong potential for future research. Our experimental results, using a dataset of 260 instances that are patient-derived and artificial, reached an outstanding RMSE of 17.8 W/kg with XgBoost, 78 W/kg with deep networks, given that the previous study on this problem reached a state-of-the-art root mean square error value (RMSE) of 168 W/kg.