This research introduces a pioneering Multiple Sclerosis (MS) lesion segmentation approach, employing a multi-scale UNet framework. The model architecture, comprised of Contraction and Expansive Paths, adeptly captures intricate details within input images. Leveraging a dataset of 60 patients MRI scans, manually segmented across three sequences, the study’s preprocessing involves slicing 3D data into 2D images, standardizing dimensions and enhancing image quality via sharpening and intensity normalization. Notably, the multi-scale UNet model achieved outstanding results, showcasing a remarkable Dice Similarity Coefficient (DSC) of 99.78% for lesion segmentation. Although the model showed decreased loss values over the training phase. The suggested approach outperformed existing algorithms like CNN, Acu-Net and ALLNet, according to a rigorous comparison analysis that looked at a number of different parameters. This study improves how doctors make decisions by making a big advance in automatically finding MS lesions. Notwithstanding challenges in conducting direct comparisons because of differences in datasets, the methodology’s proven robustness and efficacy point to its potential to revolutionize MS diagnosis and treatment.

Integrating components from convolutional neural networks and state space models in medical image segmentation presents a compelling approach to enhance accuracy and efficiency. We introduce Mamba HUNet, a novel architecture tailored for robust and efficient segmentation tasks. Leveraging strengths from Mamba UNet and the lighter version of Hierarchical Upsampling Network (HUNet), Mamba HUNet combines convolutional neural networks local feature extraction power with state space models long range dependency modeling capabilities. We first converted HUNet into a lighter version, maintaining performance parity and then integrated this lighter HUNet into Mamba HUNet, further enhancing its efficiency. The architecture partitions input grayscale images into patches, transforming them into 1D sequences for processing efficiency akin to Vision Transformers and Mamba models. Through Visual State Space blocks and patch merging layers, hierarchical features are extracted while preserving spatial information. Experimental results on publicly available Magnetic Resonance Imaging scans, notably in Multiple Sclerosis lesion segmentation, demonstrate Mamba HUNet's effectiveness across diverse segmentation tasks. The model's robustness and flexibility underscore its potential in handling complex anatomical structures. These findings establish Mamba HUNet as a promising solution in advancing medical image segmentation, with implications for improving clinical decision making processes.

Deep learning has revolutionized medical imaging by providing innovative solutions to complex healthcare challenges. Traditional models often struggle to dynamically adjust feature importance, resulting in suboptimal representation, particularly in tasks like semantic segmentation crucial for accurate structure delineation. Moreover, their static nature incurs high computational costs. To tackle these issues, we introduce Mamba-Ahnet, a novel integration of State Space Model (SSM) and Advanced Hierarchical Network (AHNet) within the MAMBA framework, specifically tailored for semantic segmentation in medical imaging.Mamba-Ahnet combines SSM's feature extraction and comprehension with AHNet's attention mechanisms and image reconstruction, aiming to enhance segmentation accuracy and robustness. By dissecting images into patches and refining feature comprehension through self-attention mechanisms, the approach significantly improves feature resolution. Integration of AHNet into the MAMBA framework further enhances segmentation performance by selectively amplifying informative regions and facilitating the learning of rich hierarchical representations. Evaluation on the Universal Lesion Segmentation dataset demonstrates superior performance compared to state-of-the-art techniques, with notable metrics such as a Dice similarity coefficient of approximately 98% and an Intersection over Union of about 83%. These results underscore the potential of our methodology to enhance diagnostic accuracy, treatment planning, and ultimately, patient outcomes in clinical practice. By addressing the limitations of traditional models and leveraging the power of deep learning, our approach represents a significant step forward in advancing medical imaging technology.

Prostate cancer is a prevalent and life-threatening disease characterized by abnormal cell growth within the prostate gland. Early and accurate diagnosis of prostate cancer is crucial for effective treatment planning. MRI is valuable for diagnosing and assessing prostate cancer. Medical professionals use MRI to create segmentation for detecting prostate cancer. However, existing segmentation methods are limited in accurately delineating anatomical structures and tumor regions within the prostate. This research proposes an innovative methodology for advancing MRI-based prostate segmentation. The objective is to encompass anatomical and tumor zones within the prostate, facilitating precise diagnosis and treatment planning. The proposed dual-pathway multi-scale hierarchical upsampling network introduces significant modifications compared to the traditional UNet-based architecture. It outperforms previous studies, demonstrating superior performance in anatomical segmentation on both the ProstateX and Prostate158 datasets. It achieves an intersection over union of 0.8449 and a dice similarity coefficient of 0.9872 on ProstateX, as well as an intersection over union of 0.8065 and a dice similarity coefficient of 0.9831 on Prostate158, suppressing previous research by a significant margin. These results highlight the potential of this approach to advance the utility of MRI in diagnosing and planning the treatment of prostate-related pathologies, benefiting both patients and healthcare practitioners.

This research investigates the Circle of Willis, a critical vascular structure vital for cerebral blood supply. We present a modified novel dual-pathway multi-scale hierarchical upsampling network (HUNet), tailored explicitly for accurate segmentation of Circle of Willis anatomical components from medical imaging data. Evaluating both the multi-label magnetic resonance angiography region of interest and the multi-label magnetic resonance angiography whole brain-case datasets, HUNet consistently outperforms the convolutional U-net model, demonstrating superior capabilities and achieving higher accuracy across various classes. Additionally, the HUNet model achieves an exceptional dice similarity coefficient of 98.61 and 97.95, along with intersection over union scores of 73.32 and 85.76 in both the multi-label magnetic resonance angiography region of interest and the multi-label magnetic resonance angiography whole brain-case datasets, respectively. These metrics highlight HUNet's exceptional performance in achieving precise and accurate segmentation of anatomical structures within the Circle of Willis, underscoring its robustness in medical image segmentation tasks. Visual representations substantiate HUNet's efficacy in delineating Circle of Willis structures, offering comprehensive insights into its superior performance.