Segmentation of echocardiographic images is an essential step for assessing the cardiac functionality and providing indicative clinical measures, and all further heart analysis relies on the accuracy of this process. However, the fuzzy nature of echocardiographic images degraded by distortion and speckle noise poses some challenges on the manual segmentation task. In this paper, we propose a fully automated left ventricle segmentation method that can overcome those challenges. Our method performs accurate delineation for the ventricle boundaries despite the ill-defined borders and shape variability of the left ventricle. The well-known deep learning segmentation model, known as the U-net, has addressed some of these challenges with outstanding performance. However, it still ignores the contribution of all semantic information through the segmentation process. Here we propose a novel deep learning segmentation method based on U-net, named ResDUnet. It incorporates feature extraction at different scales through the integration of cascaded dilated convolution. To ease the training process, residual blocks are deployed instead of the basic U-net blocks. Each residual block is enriched with a squeeze and excitation unit for channel-wise attention and adaptive feature re-calibration. The performance of the method is evaluated on a dataset of 2000 images acquired from 500 patients with large variability in quality and patient pathology. ResDUnet outperforms state-of-the-art methods with a Dice similarity increase of 8.4% and 1.2% compared to deeplabv3 and U-net, respectively. Furthermore, to demonstrate the impact of each proposed sub-module, several experiments have been carried out with different designs and variations of the integrated sub-modules. We also describe and discuss all technical elements of a deep-learning model via a step-by-step explanation of parameters and methods, while using our left ventricle segmentation as a case study, to explain the application of AI to echocardiographic imaging.

The advanced development of deep learning methods has recently made significant improvements in medical image segmentation. Encoder–decoder networks, such as U-Net, have addressed some of the challenges in medical image segmentation with an outstanding performance, which has promoted them to be the most dominating deep learning architecture in this domain. Despite their outstanding performance, we argue that they still lack some aspects. First, there is incompatibility in U-Net’s skip connection between the encoder and decoder features due to the semantic gap between low-processed encoder features and highly processed decoder features, which adversely affects the final prediction. Second, it lacks capturing multi-scale context information and ignores the contribution of all semantic information through the segmentation process. Therefore, we propose a model named MDA-Unet, a novel multi-scale deep learning segmentation model. MDA-Unet improves upon U-Net and enhances its performance in segmenting medical images with variability in the shape and size of the region of interest. The model is integrated with a multi-scale spatial attention module, where spatial attention maps are derived from a hybrid hierarchical dilated convolution module that captures multi-scale context information. To ease the training process and reduce the gradient vanishing problem, residual blocks are deployed instead of the basic U-net blocks. Through a channel attention mechanism, the high-level decoder features are used to guide the low-level encoder features to promote the selection of meaningful context information, thus ensuring effective fusion. We evaluated our model on 2 different datasets: a lung dataset of 2628 axial CT images and an echocardiographic dataset of 2000 images, each with its own challenges. Our model has achieved a significant gain in performance with a slight increase in the number of trainable parameters in comparison with the basic U-Net model, providing a dice score of 98.3% on the lung dataset and 96.7% on the echocardiographic dataset, where the basic U-Net has achieved 94.2% on the lung dataset and 93.9% on the echocardiographic dataset.

MRI-guided radiotherapy (MRIgRT) enables the real-time visualization of tumor motion, allowing adaptive radiation delivery based on dynamic anatomical changes. However, respiratory-induced tumor motion remains a major challenge, particularly for thoracic and abdominal tumors. Continuous tumor motion may reduce treatment accuracy and increase radiation exposure to surrounding healthy tissues. Therefore, reliable and efficient tumor tracking is essential for real-time motion management in MRI-guided radiotherapy. Recent advances in artificial intelligence have demonstrated significant potential for medical image analysis; however, many existing tumor tracking approaches rely on segmentation-based methods that require detailed annotations and complex processing, which can limit their use in real-time clinical environments. In this work, we propose a detection-based tumor tracking framework that integrates the YOLOv11 object detection model with an enhanced StrongSORT tracking algorithm (EA-StrongSORT). The proposed approach replaces the conventional re-identification backbone with a lightweight EfficientNetV2 architecture augmented with an Efficient Channel Attention (ECA) mechanism. The overall framework follows a tracking-by-detection concept, where tumor regions are first detected and subsequently associated across frames. The proposed framework is evaluated on the TrackRAD2025 dataset using multiple YOLOv11 variants to analyze the balance between performance and model complexity. Experimental results demonstrate that the lightweight YOLOv11n model achieves the best detection performance, with a precision of 0.912, recall of 0.607, mean Average Precision (mAP) of 0.771, and ${\mathrm{mAP}}_{50–95}$ of 0.608. Furthermore, the proposed tracking framework achieves stable temporal association, with Multiple-Object Tracking Accuracy (MOTA) scores exceeding 91% and Higher-Order Tracking Accuracy (HOTA) scores around 90%. The proposed framework demonstrates the potential of detection-based tumor localization and tracking for real-time MRI-guided radiotherapy applications.