Segnet unveiled: Robust image segmentation via rigorous K-fold cross-validation analysis.

The U-Net model for lung area segmentation in medical images is considered in this work. A preprocessed and enhanced dataset was used to train the U-Net model across 70 epochs using an encoder-decoder architecture with skip connections. To estimate the accuracy of the model, important performance indicators such as the Dice coefficient, Intersection over Union (IOU), and training/validation loss were observed. The coefficient for the dice went up from 0.5 to 0.9 in the results, while the IOU value calmed at 0.9, representing the model’s efficiency in proper segmentation. Strong generalization to previously unseen data and minimal overfitting were shown by the loss metrics’ in accordance reduction. In agreement to the study, U-Net has the potential to be used in real-world medical applications. Further study is recommended to enhance performance through the application of transfer learning and consideration devices.

Accurate intracranial artery segmentation from digital subtraction angiography (DSA) is critical for neurovascular diagnosis and intervention planning. Vascular extraction, which combines preprocessing methods and deep learning models, yields a high level of results, but limited preprocessing results constrain the improvement of results. We propose a texture-based contrast enhancement preprocessing framework integrated with the nnU-Net model to improve vessel segmentation in time-sequential DSA images. The method generates a combined feature mask by fusing local contrast, local entropy, and brightness threshold maps, which is then used as input for deep learning–based segmentation. Segmentation performance was evaluated using the DIAS dataset with various standard quantitative metrics. The proposed preprocessing significantly improved segmentation across all metrics compared to both the baseline and contrast-limited adaptive histogram equalization (CLAHE). Using nnU-Net, the method achieved a Dice Similarity Coefficient (DICE) of 0.83 ± 0.20 and an Intersection over Union (IoU) of 0.72 ± 0.14, outperforming CLAHE (DICE 0.79 ± 0.41, IoU 0.70 ± 0.23) and the baseline (DICE 0.65 ± 0.15, IoU 0.47 ± 0.20). Most notably, vessel connectivity (VC) dropped by over 65% relative to unprocessed images, indicating marked improvements in VC and topological accuracy. This study demonstrates that combining texture-based preprocessing with nnU-Net delivers robust, noise-tolerant, and clinically interpretable segmentation of intracranial arteries from DSA.

Recent studies on Parkinson's Disease (PD) discovered potential markers in brain MR images for its diagnosis and staging.One of them is the neuromelanin, which can be found in regions such as the substantia nigra and the locus coeruleus, both located in the brainstem.The analysis of these regions is normally conducted manually by a specialized professional, which can be costly and timeconsuming.Fortunately, there have been advances in automatic segmentation approaches in several areas.In medical imaging, U-Nets are showing state-of-the-art performance, but we did not find many works using this architecture to extract brainstem structures.A likely cause for this is the scarcity of large databases with well annotated segmentation of these areas.Thus, this paper explores the use of a dataset of our research group composed of T1-weighted MR images to train U-Net models with different types of slices to automatically demarcate the brainstem (as a first stage).The analysis starts with a scripted segmentation of the target region using the Freesurfer package.Thereby, we trained 4 models and evaluated them using Dice Similarity Coefficient (DSC) and Intersection over Union (IoU).Three models were trained on single anatomical planes, and one on multi-plane slices.Also, we applied grid search, varying optimizers, numbers of filters in the first layer, and learning rates.The best model trained with the larger subset with the best performance was the axial (DSC: 91.76% and IoU: 86.95%), followed by sagittal (DSC: 90.61% and IoU: 84.11%), coronal (DSC: 89.38% and IoU: 68.76%), and all slices (DSC: 16.36% and IoU: 7.58%).The next steps of our research are to differentiate the midbrain out of the brainstem, considering manual segmentation as ground truth, and test some approaches on determining the threshold and evaluating separately the 2 hemispheres of the midbrain.

Accurate segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding anatomical structures is critical for diagnosis, treatment planning, and outcome assessment. This study proposes a deep learning-based framework to automate multi-class segmentation in CT images, comparing the performance of four state-of-the-art architectures. This retrospective multi-center study included 3265 patients from six institutions. Four deep learning models-UNet, nnU-Net, UNETR, and Swin-UNet-were trained using five-fold cross-validation on data from five centers and tested independently on a sixth center (n = 569). Preprocessing included intensity normalization, voxel resampling, and standardized annotation for six structures: PDAC lesion, pancreas, veins, arteries, pancreatic duct, and common bile duct. Evaluation metrics included Dice Similarity Coefficient (DSC), Intersection over Union (IoU), directed Hausdorff Distance (dHD), Average Symmetric Surface Distance (ASSD), and Volume Overlap Error (VOE). Statistical comparisons were made using Wilcoxon signed-rank tests with Bonferroni correction. Swin-UNet outperformed all models with a mean validation DSC of 92.4% and test DSC of 90.8%, showing minimal overfitting. It also achieved the lowest dHD (4.3mm), ASSD (1.2mm), and VOE (6.0%) in cross-validation. Per-class DSCs for Swin-UNet were consistently higher across all anatomical targets, including challenging structures like the pancreatic duct (91.0%) and bile duct (91.8%). Statistical analysis confirmed the superiority of Swin-UNet (p < 0.001). All models showed generalization capability, but Swin-UNet provided the most accurate and robust segmentation across datasets. Transformer-based architectures, particularly Swin-UNet, enable precise and generalizable multi-class segmentation of PDAC and surrounding anatomy. This framework has potential for clinical integration in PDAC diagnosis, staging, and therapy planning.

Segmentation of medical images has been the most demanding and growing area currently for analysis of medical images. Segmentation of polyp images is a huge challenge because of the variability of color depth and morphology in polyps throughout colonoscopy imaging. For segmentation, in this work, we have used a dataset of images of the gastrointestinal polyp. The algorithms used in this paper for segmentation of gastrointestinal polyp images depend on profound deep convolutional neural network architectures: FCN, Dual U-net with Resnet Encoder, U-net, and Unet_Resnet. To improve the performance, data augmentation is performed on the dataset. The efficiency of the algorithms is measured by using metrics such as Dice Similarity Coefficient (DSC) and Intersection Over Union (IOU). The algorithm Dual U-net with Resnet Encoder obtains a higher DSC of 0.87 and IOU of 0.80 and beats the other algorithms U-net, FCN, and Unet_Resnet in segmentation of gastrointestinal polyp images.

ScLNet: A cornea with scleral lens OCT layers segmentation dataset and new multi-task model

Automated 3D breast ultrasound (ABUS) has substantial potential in breast imaging. ABUS appears to be beneficial because of its outstanding reproducibility and reliability, especially for screening women with dense breasts. However, due to the high number of slices in 3D ABUS, it requires lengthy screening time for radiologists, and they may miss small and subtle lesions. In this work, we propose to use a 3D Mask R-CNN method to automatically detect the location of the tumor and simultaneously segment the tumor contour. The performance of the proposed algorithm was evaluated using 25 patients' data with ABUS image and ground truth contours. To further access the performance of the proposed method, we quantified the intersection over union (IoU), Dice similarity coefficient (DSC), and center of mass distance (CMD) between the ground truth and segmentation. The resultant IoU 96% ± 2%, DSC 84% ± 3%, and CMD 1.95 ± 0.89 mm respectively, which demonstrated the high accuracy of tumor detection and 3D volume segmentation of the proposed Mask R-CNN method. We have developed a novel deep learning-based method and demonstrated its capability of being used as a useful tool for computer-aided diagnosis and treatment.

U-net-based segmentation of foreign bodies and ghost images in panoramic radiographs.

Achieving accurate bladder wall and tumor segmentation from MRI is critical for diagnosing and treating bladder cancer. However, automated segmentation remains challenging due to factors such as comparable density distributions, intricate tumor morphologies, and unclear boundaries. Considering the attributes of bladder MRI images, we propose an efficient multiscale hierarchical hybrid attention with a transformer (MH2AFormer) for bladder cancer and wall segmentation. Specifically, a multiscale hybrid attention and transformer (MHAT) module in the encoder is designed to adaptively extract and aggregate multiscale hybrid feature representations from the input image. In the decoder stage, we devise a multiscale hybrid attention (MHA) module to generate high-quality segmentation results from multiscale hybrid features. Combining these modules enhances the feature representation and guides the model to focus on tumor and wall regions, which helps to solve bladder image segmentation challenges. Moreover, MHAT utilizes the Fast Fourier Transformer with a large kernel (e.g., 224 × 224) to model global feature relationships while reducing computational complexity in the encoding stage. The model performance was evaluated on two datasets. As a result, the model achieves relatively best results regarding the intersection over union (IoU) and dice similarity coefficient (DSC) on both datasets (Dataset A: IoU = 80.26%, DSC = 88.20%; Dataset B: IoU = 89.74%, DSC = 94.48%). These advantageous outcomes substantiate the practical utility of our approach, highlighting its potential to alleviate the workload of radiologists when applied in clinical settings.

To investigate the potential of deep learning for automatically delineating (segmenting) laryngeal cancer superficial extent on endoscopic images and videos. A retrospective study was conducted extracting and annotating white light (WL) and Narrow-Band Imaging (NBI) frames to train a segmentation model (SegMENT-Plus). Two external datasets were used for validation. The model's performances were compared with those of two otolaryngology residents. In addition, the model was tested on real intraoperative laryngoscopy videos. A total of 3933 images of laryngeal cancer from 557 patients were used. The model achieved the following median values (interquartile range): Dice Similarity Coefficient (DSC) = 0.83 (0.70-0.90), Intersection over Union (IoU) = 0.83 (0.73-0.90), Accuracy = 0.97 (0.95-0.99), Inference Speed = 25.6 (25.1-26.1) frames per second. The external testing cohorts comprised 156 and 200 images. SegMENT-Plus performed similarly on all three datasets for DSC (p = 0.05) and IoU (p = 0.07). No significant differences were noticed when separately analyzing WL and NBI test images on DSC (p = 0.06) and IoU (p = 0.78) and when analyzing the model versus the two residents on DSC (p = 0.06) and IoU (Senior vs. SegMENT-Plus, p = 0.13; Junior vs. SegMENT-Plus, p = 1.00). The model was then tested on real intraoperative laryngoscopy videos. SegMENT-Plus can accurately delineate laryngeal cancer boundaries in endoscopic images, with performances equal to those of two otolaryngology residents. The results on the two external datasets demonstrate excellent generalization capabilities. The computation speed of the model allowed its application on videolaryngoscopies simulating real-time use. Clinical trials are needed to evaluate the role of this technology in surgical practice and resection margin improvement. III Laryngoscope, 134:2826-2834, 2024.

Floods are the most frequently occurring natural disasters, significantly impacting the environment and society. As part of natural disaster mitigation, the impacts could be reduced through predictive techniques using deep learning for semantic segmentation of inundation images. Therefore, this research aims to evaluate the performance of deep learning architectures in segmenting inundation images using the Flood Segmentation dataset, which comprised 290 aerial images. The following segmentation architectures, U-Net, SegNet, and LinkNet, were compared using backbones such as MobileNet, ResNet, EfficientNet, and VGG, as well as optimizers including Adam, SGD, AdaDelta, and RMSProp. Performance was assessed using Intersection over Union (IoU) score, precision, F1-score, recall, and accuracy metrics. The results showed that U-Net achieved the highest performance with IoU, precision, F1-score, recall, and accuracy of 0.767, 0.862, 0.866, 0.876, and 0.899, respectively. Regarding the backbones, MobileNet excelled with IoU, precision, F1-score, recall, and accuracy of 0.764, 0.866, 0.865, 0.869, and 0.898, respectively. The Adam optimizer outperformed others, yielding IoU, precision, F1-score, recall, and accuracy of 0.712, 0.807, 0.824, 0.873, and 0.843. In conclusion, the combination of U-Net with MobileNet backbone and Adam optimizer was the most effective architecture for flood inundation image segmentation, offering a robust foundation for prediction systems.

Accurate lesion segmentation is critical for cancer diagnosis and treatment. Convolutional neural networks (CNNs) are widely used for medical image segmentation but struggle to capture long-range dependencies. Transformers mitigate this limitation but come with high computational costs. Mamba, a state-space model (SSM), efficiently models long-range dependencies but lacks precision in fine details. To address these challenges, this study aimed to develop a novel segmentation approach that combines the strengths of CNNs, Transformers, and Mamba, enhancing both global context understanding and local feature extraction in medical image segmentation. We propose AttmNet, a U-shaped network designed for medical image segmentation, which incorporates a novel structure called MAM (Multiscale-Convolution, Self-Attention, and Mamba). The MAM block integrates multi-layer convolution for multi-scale feature learning with an Att-Mamba component that combines self-attention and Mamba to effectively capture global context while preserving fine details. We evaluated AttmNet on four public datasets for breast, skin, and lung lesion segmentation. AttmNet outperformed state-of-the-art methods in terms of intersection over union (IoU) and Dice similarity coefficients. On the breast ultrasound (BUS) dataset, AttmNet achieved a 3.38% improvement in IoU and a 4.54% increase in Dice over the next best method. On the breast ultrasound images (BUSI) dataset, AttmNet's IoU and Dice coefficients were 1.17% and 3.21% higher than the closest competitor, respectively. In the PH2 Dermoscopy Image dataset, AttmNet surpassed the next best model by 0.25% in both IoU and Dice. On the larger coronavirus disease 2019 (COVID-19) Lung dataset, AttmNet maintained strong performance, achieving higher IoU and Dice scores than the next best models, SegMamba and TransUNet. AttmNet is a powerful and efficient tool for medical image segmentation, addressing the limitations of existing methods through its advanced design. The MAM block significantly enhances segmentation accuracy while maintaining computational efficiency, making AttmNet highly suitable for clinical applications. The code is available at https://github.com/hyb2840/AttmNet.

Blood cell classification and counting are vital for the diagnosis of various blood-related diseases, such as anemia, leukemia, lymphoma, and thrombocytopenia. The manual process of blood cell classification and counting is time-consuming, prone to errors, and labor-intensive. Therefore, we have proposed a deep learning (DL)-based automated system for blood cell classification and counting from microscopic blood smear images. We classify a total of nine types of blood cells, including Erythrocyte, Erythroblast, Neutrophil, Basophil, Eosinophil, Lymphocyte, Monocyte, Immature Granulocytes, and Platelet. Several preprocessing steps like image resizing, rescaling, contrast enhancement and augmentation are utilized. To segment the blood cells from the entire microscopic images, we employed the U-Net model. This segmentation technique aids in extracting the region of interest (ROI) by removing complex and noisy background elements. Both pixel-level metrics such as accuracy, precision, and sensitivity, and object-level evaluation metrics like Intersection over Union (IOU) and Dice coefficient are considered to comprehensively evaluate the performance of the U-Net model. The segmentation model achieved impressive performance metrics, including 98.23% accuracy, 98.40% precision, 98.26% sensitivity, 95.97% Intersection over Union (IOU), and 97.92% Dice coefficient. Subsequently, a watershed algorithm is applied to the segmented images to separate overlapped blood cells and extract individual cells. We have proposed a BloodCell-Net approach incorporated with custom light weight convolutional neural network (LWCNN) for classifying individual blood cells into nine types. Comprehensive evaluation of the classifier’s performance is conducted using metrics including accuracy, precision, recall, and F1 score. The classifier achieved an average accuracy of 97.10%, precision of 97.19%, recall of 97.01%, and F1 score of 97.10%. A 5-fold cross-validation technique is applied to split the data, which not only aids in reducing overfitting but also helps in generalizing the model.

Chronic wounds cause a significant burden to affected patients and to society. Effective and objective diagnostic and monitoring methods are needed in wound care, and artificial intelligence offers one promising alternative. In this study, real-world wound images were used to train a convolutional neural network to automatically segment wound area and wound tissues on an image. The study included altogether 362 images of venous, arterial, vasculitis and pyoderma gangrenosum wounds. The model was based on a convolutional neural network architecture U-Net, and fully supervised learning was utilised during the training phase. Wound area reached a Dice Similarity Coefficient (DSC) of 0.927 and Intersection over Union (IoU) of 0.868 using an augmented dataset with pretraining. Fibrinous exudate and granulation performed fairly well with DSC 0.750 and 0.696, and with IoU 0.659 and 0.601, respectively. Necrosis present in only 56 images achieved lower performance with DSC 0.503 and IoU 0.502. In conclusion, this study suggested that it is possible to train a neural network to perform well with images taken for purely clinical purposes. Besides wound area, several wound structures can be identified, but wound structure identification performance is dependent on the number of images featuring the structure.

IntroductionNarrow Band Imaging (NBI) is an endoscopic visualization technique useful for upper aero-digestive tract (UADT) cancer detection and margins evaluation. However, NBI analysis is strongly operator-dependent and requires high expertise, thus limiting its wider implementation. Recently, artificial intelligence (AI) has demonstrated potential for applications in UADT videoendoscopy. Among AI methods, deep learning algorithms, and especially convolutional neural networks (CNNs), are particularly suitable for delineating cancers on videoendoscopy. This study is aimed to develop a CNN for automatic semantic segmentation of UADT cancer on endoscopic images.Materials and MethodsA dataset of white light and NBI videoframes of laryngeal squamous cell carcinoma (LSCC) was collected and manually annotated. A novel DL segmentation model (SegMENT) was designed. SegMENT relies on DeepLabV3+ CNN architecture, modified using Xception as a backbone and incorporating ensemble features from other CNNs. The performance of SegMENT was compared to state-of-the-art CNNs (UNet, ResUNet, and DeepLabv3). SegMENT was then validated on two external datasets of NBI images of oropharyngeal (OPSCC) and oral cavity SCC (OSCC) obtained from a previously published study. The impact of in-domain transfer learning through an ensemble technique was evaluated on the external datasets.Results219 LSCC patients were retrospectively included in the study. A total of 683 videoframes composed the LSCC dataset, while the external validation cohorts of OPSCC and OCSCC contained 116 and 102 images. On the LSCC dataset, SegMENT outperformed the other DL models, obtaining the following median values: 0.68 intersection over union (IoU), 0.81 dice similarity coefficient (DSC), 0.95 recall, 0.78 precision, 0.97 accuracy. For the OCSCC and OPSCC datasets, results were superior compared to previously published data: the median performance metrics were, respectively, improved as follows: DSC=10.3% and 11.9%, recall=15.0% and 5.1%, precision=17.0% and 14.7%, accuracy=4.1% and 10.3%.ConclusionSegMENT achieved promising performances, showing that automatic tumor segmentation in endoscopic images is feasible even within the highly heterogeneous and complex UADT environment. SegMENT outperformed the previously published results on the external validation cohorts. The model demonstrated potential for improved detection of early tumors, more precise biopsies, and better selection of resection margins.