Platelets play a crucial role in diagnosing and detecting various diseases, influencing the progression of conditions and guiding treatment options. Accurate identification and classification of platelets are essential for these purposes. The present study aims to create a synthetic database of platelet images using Generative Adversarial Networks (GANs) and validate its effectiveness by comparing it with datasets of increasing sizes generated through traditional augmentation techniques. Starting from an initial dataset of 71 platelet images, the dataset was expanded to 141 images (Level 1) using random oversampling and basic transformations and further to 1463 images (Level 2) through extensive augmentation (rotation, shear, zoom). Additionally, a synthetic dataset of 300 images was generated using a Wasserstein GAN with Gradient Penalty (WGAN-GP). Eight pre-trained deep learning models (DenseNet121, DenseNet169, DenseNet201, VGG16, VGG19, InceptionV3, InceptionResNetV2, and AlexNet) and two custom CNNs were evaluated across these datasets. Performance was measured using accuracy, precision, recall, and F1-score. On the extensively augmented dataset (Level 2), InceptionV3 and InceptionResNetV2 reached 99% accuracy and 99% precision/recall/F1-score, while DenseNet201 closely followed, with 98% accuracy, precision, recall and F1-score. GAN-augmented data further improved DenseNet’s performance, demonstrating the potential of GAN-generated images in enhancing platelet classification, especially where data are limited. These findings highlight the benefits of combining traditional and GAN-based augmentation techniques to improve classification performance in medical imaging tasks.

Head immobilization thermoplastic masks for radiotherapy purposes involve a distressful modelling procedure for the patient. To assess the possibility of using different acquisition and reconstruction methods to obtain a 3Dskin surface model of PIXY-phantom-head and to present a proposal of an alternative head immobilisation mask prototype. Phantom head geometry acquisitions using: computed tomography (reconstructed with ImageJ and Osirix); and 3DLaserScanner (reconstructed with SolidWorks). From these reconstructed surface models a set of landmarks was measured and subsequently compared with physical measurements obtained with a Rosscraft-Calliper. For statistical evaluation, relative deviations graphics and Friedman-test for non-parametrical paired samples were used, with a significance level of 5%. For a first assessment of the proposed mask performance, a radiotransparent material was considered, the strength and stiffness evaluation being performed using the finite element method. There are small differences between all the acquisitions and reconstructions methods and the physical measurements, statistically significant differences (X2F(6)) = 6.863, p=0.334) were not found. The proposed mask performed well from the strength and stiffness perspectives, leading to the desired immobilisation aim. The immobilisation mask design proposal may be an effective alternative to the present completely hand-made situation, which presents a high-degree of discomfort and stress to the patients.

Manual counting of platelets, in microscopy images, is greatly time-consuming. Our goal was to automatically segment and count platelets images using a deep learning approach, applying U-Net and Fully Convolutional Network (FCN) modelling. Data preprocessing was done by creating binary masks and utilizing supervised learning with ground-truth labels. Data augmentation was implemented, for improved model robustness and detection. The number of detected regions was then retrieved as a count. The study investigated the U-Net models performance with different datasets, indicating notable improvements in segmentation metrics as the dataset size increased, while FCN performance was only evaluated with the smaller dataset and abandoned due to poor results. U-Net surpassed FCN in both detection and counting measures in the smaller dataset Dice 0.90, accuracy of 0.96 (U-Net) vs Dice 0.60 and 0.81 (FCN). When tested in a bigger dataset U-Net produced even better values (Dice 0.99, accuracy of 0.98). The U-Net model proves to be particularly effective as the dataset size increases, showcasing its versatility and accuracy in handling varying cell sizes and appearances. These data show potential areas for further improvement and the promising application of deep learning in automating cell segmentation for diverse life science research applications.

Existing automated deep learning frameworks for cellular analysis, such as the standard U-Net and Fully Convolutional Networks (FCN), face significant performance degradation when processing clinical samples with high morphological variance, dense clumping, and low-contrast borders. While advanced iterations like Transformer-based U-Nets and pure Kolmogorov-Arnold Networks (KANs) attempt to address these limitations, they introduce substantial technical gaps. Transformers suffer from prohibitive computational overhead and demand massive annotated datasets, whereas pure spline-based models are highly sensitive to noise and introduce severe memory strain. Consequently, existing automated models struggle to maintain fine-grained boundary definitions without suffering from edge-blurring pitfalls or structural inefficiency in aggregated cell environments.To address these limitations, this study evaluates an improved and more effective Kolmogorov-Arnold Attention U-Net (KC-UNet) architecture. This framework integrates learnable B-spline activation functions directly into a localized two-dimensional latent bottleneck to resolve overlapping junctions, while utilizing Convolutional Block Attention Modules along the skip connections to suppress background noise and enhance multi-scale feature routing. The performance of this system was evaluated against standard baseline models, including U-Net, FCN, and DeepLabV3, using a rigorous nested 5-fold cross-validation strategy across both a static original dataset and an augmented dataset injected with simulated clinical artifacts.The empirical results demonstrate a notable improvement in both performance resilience and clinical utility. Under dataset augmentation, standard models suffered severe performance drop-offs, with U-Net and DeepLabV3 yielding negative mean Intersection over Union (mIoU) delta changes. In contrast, the KC-UNet was the only architecture to maintain structural stability under clinical noise, demonstrating a positive resilience change of +0.0133 mIoU. Downstream instance counting accuracy further validated its clinical value, with the architecture achieving the lowest Mean Absolute Error (MAE) of 13.15, significantly outperforming the standard U-Net (MAE of 23.50) and DeepLabV3 (MAE of 30.40). Validation analysis additionally confirmed that this framework provides the narrowest error variance and a 50% reduction in systematic counting bias compared to baseline convolutional models.This research provides a highly robust, parameter-efficient framework for automated hematological diagnostics, demonstrating that combining the local feature-extraction capabilities of convolutions with the non-linear flexibility of learnable spline edges resolves dense platelet clumping effectively. By mitigating operator bias and batch-dependent manual threshold adjustments, this strategy advances automated high-throughput clinical screening pipelines. Future work will focus on structural model pruning for embedded clinical deployment, accelerating edge computations, and exploring self-supervised learning paradigms to eliminate the pipeline's reliance on pseudo-ground truth masks.

Platelets undergo significant morphological transformations during activation influenced by chemical compounds such as zinc and milrinone. Manual classification methods are subjective, time-consuming, and prone to error. This research aimed to apply and evaluate the efficacy of different pre-trained deep learning Convolution Neural Networks, used for automated binary (treated vs. untreated) and multiclass classification (control vs. zinc vs. milrinone vs. combination) of platelet morphological changes induced by these solvents, addressing the limitations of manual classification methods. A dataset of 176 microscopic platelet images from Hull York Medical School was processed, comprising control (n=43), zinc-treated (n=45), milrinone-treated (n=40), and combination treatment (n=48) samples. Ten pre-trained CNN architectures were evaluated across 166 individual training iterations for both binary and multiclass classification tasks. Progressive optimisation strategies included contrast-limited adaptive histogram equalisation (CLAHE) enhancement, selective layer unfreezing, cross-validation, and hyperparameter tuning. For binary classification, Xception achieved optimal balanced performance (81% accuracy, 76% precision, 74% recall) with proper inter-class discrimination, despite higher numerical metrics (91% accuracy) showing classification bias. For multiclass classification, InceptionV3 and Xception demonstrated superior effectiveness with 72% accuracy. This study demonstrated that Xception and InceptionV3 architectures effectively capture subtle morphological features distinguishing treated platelets, offering reproducible alternatives to subjective manual methods.

Platelets undergo significant morphological transformations during activation influenced by chemical compounds such as zinc and milrinone. Manual classification methods are subjective, time-consuming, and prone to error. This research aimed to apply and evaluate the efficacy of different pre-trained deep learning Convolution Neural Networks, used for automated binary (treated vs untreated) and multiclass classification (control vs zinc vs milrinone vs combination) of platelet morphological changes induced by these solvents, addressing the limitations of manual classification methods. A dataset of 176 microscopic platelet images from Hull York Medical School was processed, comprising control (n = 43), zinc-treated (n = 45), milrinone-treated (n = 40), and combination treatment (n = 48) samples. Ten pre-trained CNN architectures were evaluated across 166 individual training iterations for both binary and multiclass classification tasks. Progressive optimisation strategies included contrast-limited adaptive histogram equalisation (CLAHE) enhancement, selective layer unfreezing, cross-validation, and hyperparameter tuning. For binary classification, Xception achieved optimal balanced performance (81% accuracy, 76% precision, 74% recall) with proper inter-class discrimination, despite higher numerical metrics (91% accuracy) showing classification bias. For multiclass classification, InceptionV3 and Xception demonstrated superior effectiveness with 72% accuracy. This study demonstrated that Xception and InceptionV3 architectures effectively capture subtle morphological features distinguishing treated platelets, offering reproducible alternatives to subjective manual methods.