Design of U-Net Architectures for Medical Image Segmentation using AI/ML Model

MRD-UNet: a compute-efficient medical image segmentation architecture with multi-attention and parallel dilated fusion

MRD-UNet provides a practical balance between segmentation accuracy and computational efficiency and outperforms baseline CNNs and performs comparably to heavier transformer-based models while using significantly fewer parameters.

Medical image segmentation with optimal learning from limited data and annotations: A comprehensive review

This survey extends beyond traditional and deep learning-based augmentation techniques or deep semi-supervised approaches, by explicitly focusing on medical/clinical imaging modalities, by explicitly focusing on CT, MRI, and X-ray, offering a broader perspective.

A Deep Learning-Driven Brain Tumour Segmentation using a Hybrid U-Net and LSTM Architecture

The most important step in diagnosis, treatment planning, and prediction analysis is the accurate segmentation of brain tumours from magnetic resonance imaging (MRI) scans. Radiologists' manual segmentation was time-consuming, subjective, and inaccurate. For this reason, there is a need for automated approaches. In Current days, deep learning (DL) has shown great promise for enhancing the processing of medical images. In DL, the U-Net architecture has become a typical framework for medical image segmentation of images due to its symmetric encoder–decoder design and skip links that preserve spatial detail. At the same time, conventional U-Net models are restricted to 2D slices and cannot detect contextual connections between slices in spatial MRI images; this may lead to discontinuities and reduced accuracy. The study suggests addressing the above-mentioned challenges, a hybrid deep learning framework consisting of a U-Net architecture integrated with Long Short-Term Memory (LSTM) networks is designed. The U-Net component extracts the variant-invariant spatial and structural features, and LSTM is responsible for integrating the temporal dependencies spatially, which inject discontinuities across parallel slices, which is imperative for the boundary delineation and localisation of the tumour. Compared to the baseline model, the suggested hybrid U-Net and LSTM networks exhibit noticeably better segmentation accuracy and visual consistency under various scenarios after being trained and assessed on a publicly accessible brain MRI dataset. According to experimental data, the suggested model provides great segmentation performance, with Dice scores above 95% and accuracy above 96%. To sum up, the whole exercise displays the promise of combining a convolutional and a recurrent architecture to propagate automated neuroimaging analysis. This will not only reduce the manual labour but also continue to work well in clinical practice, as it is comfortable and prepared for replication in the future.

Potential-Guided Connected Network for Tiny Structure Segmentation in Medical Images

Medical images provide essential information for diagnosing and monitoring various diseases and systemic disorders. With advancements in deep learning and neural networks, numerous methods have been proposed to achieve high-level medical image segmentation results. However, the variability of tiny structures and their high similarity to the background often lead to mis-segmentation in existing methods. To mitigate these challenges, we propose a potential-guided connected network (PCNet) that integrates an innovative dual soft-hard constraint strategy, combining two different progressive supervisions. This strategy modulates the ability of network to differentiate between well-defined and ambiguous structures through a hyper-parameter, thereby enhancing its capability to detect tiny structures. Furthermore, PCNet is composed of two key modules, including the intermediate generation (IG) module and the progressive inference (PI) module. The IG module produces a range of outputs with varying segmentation potentials using a novel serial architecture, which serves as the foundational input for progressive reasoning in the PI module. The PI module, leveraging the outputs of the IG module, is designed to progressively extract comprehensive contextual information, ultimately producing refined segmentation results. PCNet is evaluated on several publicly available datasets, including DRIVE, MoNuSeg, CoNIC, FIVES, and GlaS, achieving accuracy of 96.92%, 90.29%, 93.93%, 98.82%, and 92.00%, respectively. Extensive experiments demonstrate that our model outperforms the current state-of-the-art methods for tiny structure segmentation in medical images.

HCDU-net and the shape of precision: advancing liver tumor segmentation with statistical geometry and adaptive deep learning using U-net

Dual U-shaped cross-modal fusion network for lung infection region segmentation