The human liver exhibits variable characteristics and anatomical information, which is often ambiguous in radiological images. Machine learning can be of great assistance in automatically segmenting the liver in radiological images, which can be further processed for computer-aided diagnosis. Magnetic resonance imaging (MRI) is preferred by clinicians for liver pathology diagnosis over volumetric abdominal computerized tomography (CT) scans, due to their superior representation of soft tissues. The convenience of Hounsfield unit (HoU) based preprocessing in CT scans is not available in MRI, making automatic segmentation challenging for MR images. This study investigates multiple state-of-the-art segmentation networks for liver segmentation from volumetric MRI images. Here, T1-weighted (in-phase) scans are investigated using expert-labeled liver masks from a public dataset of 20 patients (647 MR slices) from the Combined Healthy Abdominal Organ Segmentation grant challenge (CHAOS). The reason for using T1-weighted images is that it demonstrates brighter fat content, thus providing enhanced images for the segmentation task. Twenty-four different state-of-the-art segmentation networks with varying depths of dense, residual, and inception encoder and decoder backbones were investigated for the task. A novel cascaded network is proposed to segment axial liver slices. The proposed framework outperforms existing approaches reported in the literature for the liver segmentation task (on the same test set) with a dice similarity coefficient (DSC) score and intersect over union (IoU) of $95.15\%$ and $92.10\%$, respectively.

Background: Portomesenteric Vein Thrombosis (PMVT) following Laparoscopic Sleeve Gastrectomy (LSG) is an uncommon but potentially debilitating complication. Catheter-Directed Thrombolysis (CDT) has an evolving role in recanalizing the venous flow and preventing thrombus propagation. Therefore, it can be used as an alternative or in combination with systemic anticoagulants in selected patients. We report two trans-hepatic and trans-splenic CDT. The patient’s clinical details, radiological findings, safety, and efficacy are reported. Cases presentation: Two patients presented to the Emergency Department (ED) within 14 days of surgery. The presenting complaints were generally nonspecific. The diagnosis of PMVT was established in both patients based on abdominal Contrast-Enhanced Computed Tomography (CECT). The two patients received a combined therapy of subcutaneous (SC) heparinization and CDT using a trans-hepatic approach in case 1 and a trans-splenic approach in case 2. Subsequent post-procedure venograms and CECT were performed and showed significant thrombus resolution. Both patients received oral anticoagulant therapy upon discharge with a successful overall recovery. Conclusion: PMVT is an infrequent and severe post LSG complication. Various approaches for re-establishing the portal venous flow have been described according to the severity of venous thrombosis. This article describes CDT therapy as a safe and effective option for treating PMVT in symptomatic patients.

Digital pathology relies on the morphological architecture of prostate glands to recognize cancerous tissue. Prostate cancer (PCa) originates in walnut shaped prostate gland in the male reproductive system. Deep learning (DL) pipelines can assist in identifying these regions with advanced segmentation techniques which are effective in diagnosing and treating prostate diseases. This facilitates early detection, targeted biopsy, and accurate treatment planning, ensuring consistent, reproducible results while minimizing human error. Automated segmentation techniques trained on MRI datasets can aid in monitoring disease progression which leads to clinical support by developing patient-specific models for personalized medicine. In this study, we present multiclass segmentation models designed to localize the prostate gland and its zonal regions-specifically the peripheral zone (PZ), transition zone (TZ), and the whole gland-by combining EfficientNetB4 encoders with Self-organized Operational Neural Network (Self-ONN)-based decoders. Traditional convolutional neural networks (CNNs) rely on linear neuron models, which limit their ability to capture the complex dynamics of biological neural systems. In contrast, Operational Neural Networks (ONNs), particularly Self-ONNs, address this limitation by incorporating nonlinear and adaptive operations at the neuron level. We evaluated various encoder-decoder configurations and identified that the combination of an EfficientNet-based encoder with a Self-ONN-based decoder yielded the best performance. To further enhance segmentation accuracy, we employed the STAPLE method to ensemble the top three performing models. Our approach was tested on the large-scale, recently updated PI-CAI Challenge dataset using 5-fold cross-validation, achieving Dice scores of 95.33 % for the whole gland and 92.32 % for the combined PZ and TZ regions. These advanced segmentation techniques significantly improve the quality of PCa diagnosis and treatment, contributing to better patient care and outcomes.

Ischemic stroke, caused by cerebral vessel occlusion, presents substantial challenges in medical imaging due to the variability and subtlety of stroke lesions. Magnetic Resonance Imaging (MRI) plays a crucial role in diagnosing and managing ischemic stroke, yet existing segmentation techniques often fail to accurately delineate lesions. This study introduces a novel deep learning-based method for segmenting ischemic stroke lesions using multi-channel MRI modalities, including Diffusion Weighted Imaging (DWI), Apparent Diffusion Coefficient (ADC), and enhanced Diffusion Weighted Imaging (eDWI). The proposed architecture integrates DenseNet121 as the encoder with Self-Organized Operational Neural Networks (SelfONN) in the decoder, enhanced by Channel and Space Compound Attention (CSCA) and Double Squeeze-and-Excitation (DSE) blocks. Additionally, a custom loss function combining Dice Loss and Jaccard Loss with weighted averages is introduced to improve model performance. Trained and evaluated on the ISLES 2022 dataset, the model achieved Dice Similarity Coefficients (DSC) of 83.88% using DWI alone, 85.86% with DWI and ADC, and 87.49% with the integration of DWI, ADC, and eDWI. This approach not only outperforms existing methods but also addresses key limitations in current segmentation practices. These advancements significantly enhance diagnostic precision and treatment planning for ischemic stroke, providing valuable support for clinical decision-making.

Background/Objectives: This review and meta-analysis assessed whether combining transarterial chemoembolization (TACE) with iodine-125 brachytherapy (I-125 brachytherapy) offers greater efficacy and safety than TACE alone in treating hepatocellular carcinoma (HCC). Methods: PubMed, EMBASE, the Cochrane Library, Scopus, and Web of Science were searched for articles published between 1 January 2010 and 30 November 2023. Eligible studies compared TACE with and without I-125 brachytherapy from randomized controlled trials (RCTs) and non-randomized comparative studies published in English. The primary outcome was overall survival (OS) at 1, 2, and 3 years. The secondary outcomes included progression-free survival (PFS), overall response rate (ORR), disease control rate (DCR), and adverse events. ROB-2 and ROBINS-I tools were used to assess study quality. Results: Eighteen studies (n = 1872 patients) were included. All 18 studies originated from China, with the majority being retrospective cohorts (n = 16), one non-randomized prospective study, and one RCT. Compared with TACE alone, TACE + I-125 brachytherapy significantly improved OS at 1 year (OR = 3.64, 95% CI: 2.92–4.55), 2 years (OR = 3.93, 95% CI: 2.29–6.77), and 3 years (OR = 4.12, 95% CI: 2.24–7.56). The tumor response rates, including the ORR and DCR, were also significantly higher in the combination group. Subgroup analysis revealed that the survival benefit was maintained in studies without systemic chemotherapy (OR = 3.68, 95% CI: 2.89–4.70) and in studies with systemic chemotherapy (OR = 4.13, 95% CI: 1.69–10.09). Although larger effect estimates were observed with low-dose I-125 brachytherapy (<80 Gy; OR = 8.55, 95% CI: 4.32–16.92) compared to high-dose (≥100 Gy; OR = 2.87, 95% CI: 2.05–4.00), this finding is hypothesis-generating rather than conclusive and should be interpreted cautiously as it is based on only three studies. Adverse event rates were comparable between groups. GRADE assessment indicated low to very low certainty for all outcomes, primarily due to the retrospective nature of most included studies. Conclusions: TACE combined with I-125 brachytherapy was associated with improved survival and tumor response without a statistically significant increase in adverse events. High-quality, multicenter RCTs are warranted to confirm these results.

Automated lumbar spine segmentation is very crucial for modern diagnosis systems. In this study, we introduce a novel machine-agnostic approach for segmenting lumbar vertebrae and intervertebral discs from MRI images, employing a cascaded model that synergizes an ROI detection and a Self-organized Operational Neural Network (Self-ONN)-based encoder-decoder network for segmentation. Addressing the challenge of diverse MRI modalities, our methodology capitalizes on a unique dataset comprising images from 12 scanners and 34 subjects, enhanced through strategic preprocessing and data augmentation techniques. The YOLOv8 medium model excels in ROI extraction, achieving an excellent performance of 0.916 mAP score. Significantly, our Self-ONN-based model, combined with a DenseNet121 encoder, demonstrates excellent performance in lumbar vertebrae and IVD segmentation with a mean Intersection over Union (IoU) of 83.66%, a sensitivity of 91.44%, and Dice Similarity Coefficient (DSC) of 91.03%, as validated through rigorous 10-fold cross-validation. This study not only showcases an effective approach to MRI segmentation in spine-related disorders but also sets the stage for future advancements in automated diagnostic tools, emphasizing the need for further dataset expansion and model refinement for broader clinical applicability.