Comparative Study between Gluteal Artery Perforator Flaps and Local Fasciocutaneous Flaps in Reconstruction of Gluteal Pressure Ulcers.

Background: Gluteal pressure ulcers are a common problem, associated with great morbidity and cost, and their surgical treatment includes debridement with complete bursectomy, followed by soft tissue coverage. Gluteal artery perforator flaps and gluteal fasciocutaneous flaps are commonly preferred for reconstruction because they preserve the gluteal muscle, allowing for revision in recurrent cases. The aim of this study was to evaluate the differences between these two flaps in the reconstruction of gluteal pressure ulcers regarding operative time, postoperative hospital stay, postoperative complications, and recurrence. Methods: This prospective comparative study was conducted on 30 patients who presented with stage IV gluteal pressure ulcers. Patients were randomly allocated into two equal groups: each group consisted of 15 patients. Cases in group A were reconstructed using gluteal artery perforator flaps, and those in group B were reconstructed using local fasciocutaneous flaps. Results: There was statistically significant long operative time and short postoperative hospital stay in gluteal artery perforator flaps when compared with local fasciocutaneous flaps. Also, the fasciocutaneous group reported a higher nonsignificant complication rate when compared with the gluteal perforator group. No recurrent cases were reported, and most patients had satisfactory outcomes in both groups. Conclusion: Both techniques are safe, reliable, and effective and can be considered as a first-line option in the reconstruction of gluteal pressure ulcers.

Using a neural network model to evaluate the mechanical and tribological properties of vermicular cast iron based on hardness.

Measuring the tensile strength, wear resistance, and impact strength of metals, particularly cast iron, is complex and more expensive than performing hardness tests. In the present study, owing to the ease of specimen preparation and low cost, the Hardness (HB) test was used to approximately predict Wear Rate (WR), Impact Energy (IE), and tensile strength (TS). The relation between Mg% and HB, tensile strength, WR, and IE was examined by using three experimental groups of compacted graphite cast iron (CGI) treated with a nodulizer (Fe-Si-Mg) alloy at different carbon equivalents (CEs) of 3.5, 4.0, and 4.5 %. The produced CGI exhibited HB, TS, WR, and IE of 191-226 HB, 402-455 MPa, 30.1-23.8 mg/cm2, and 22-15 J, respectively. The good results were taken at a CE of 4.5 % and Mg content of 0.0118-0.0155 %. the regression analysis and artificial neural network model (ANNs) were used in the hardness test, and the results indicated the possibility of predicting IE, WR, tensile strength, and high accuracy Mg% of the produced CGI. It could be observed that, the neural network algorithm model has a high prediction precision for determining the Mg% content and the properties of the prepared CGI based on hardness. In the case of CE = 4, the MSE calculated for the predicted and measured data taken from the used ANNs model is 3.7 E-8, 20.33, 0.3084, and 0.099 for Mg%, TS, WR, and IE, respectively.

Effective Approaches to Fetal Brain Segmentation in MRI and Gestational Age Estimation by Utilizing a Multiview Deep Inception Residual Network and Radiomics.

To completely comprehend neurodevelopment in healthy and congenitally abnormal fetuses, quantitative analysis of the human fetal brain is essential. This analysis requires the use of automatic multi-tissue fetal brain segmentation techniques. This paper proposes an end-to-end automatic yet effective method for a multi-tissue fetal brain segmentation model called IRMMNET. It includes a inception residual encoder block (EB) and a dense spatial attention (DSAM) block, which facilitate the extraction of multi-scale fetal-brain-tissue-relevant information from multi-view MRI images, enhance the feature reuse, and substantially reduce the number of parameters of the segmentation model. Additionally, we propose three methods for predicting gestational age (GA)-GA prediction by using a 3D autoencoder, GA prediction using radiomics features, and GA prediction using the IRMMNET segmentation model's encoder. Our experiments were performed on a dataset of 80 pathological and non-pathological magnetic resonance fetal brain volume reconstructions across a range of gestational ages (20 to 33 weeks) that were manually segmented into seven different tissue categories. The results showed that the proposed fetal brain segmentation model achieved a Dice score of 0.791±0.18, outperforming the state-of-the-art methods. The radiomics-based GA prediction methods achieved the best results (RMSE: 1.42). We also demonstrated the generalization capabilities of the proposed methods for tasks such as head and neck tumor segmentation and the prediction of patients' survival days.

An Enhanced Scheme for Reducing the Complexity of Pointwise Convolutions in CNNs for Image Classification Based on Interleaved Grouped Filters without Divisibility Constraints.

In image classification with Deep Convolutional Neural Networks (DCNNs), the number of parameters in pointwise convolutions rapidly grows due to the multiplication of the number of filters by the number of input channels that come from the previous layer. Existing studies demonstrated that a subnetwork can replace pointwise convolutional layers with significantly fewer parameters and fewer floating-point computations, while maintaining the learning capacity. In this paper, we propose an improved scheme for reducing the complexity of pointwise convolutions in DCNNs for image classification based on interleaved grouped filters without divisibility constraints. The proposed scheme utilizes grouped pointwise convolutions, in which each group processes a fraction of the input channels. It requires a number of channels per group as a hyperparameter Ch. The subnetwork of the proposed scheme contains two consecutive convolutional layers K and L, connected by an interleaving layer in the middle, and summed at the end. The number of groups of filters and filters per group for layers K and L is determined by exact divisions of the original number of input channels and filters by Ch. If the divisions were not exact, the original layer could not be substituted. In this paper, we refine the previous algorithm so that input channels are replicated and groups can have different numbers of filters to cope with non exact divisibility situations. Thus, the proposed scheme further reduces the number of floating-point computations (11%) and trainable parameters (10%) achieved by the previous method. We tested our optimization on an EfficientNet-B0 as a baseline architecture and made classification tests on the CIFAR-10, Colorectal Cancer Histology, and Malaria datasets. For each dataset, our optimization achieves a saving of 76%, 89%, and 91% of the number of trainable parameters of EfficientNet-B0, while keeping its test classification accuracy.

Predicting Breast Tumor Malignancy Using Deep ConvNeXt Radiomics and Quality-Based Score Pooling in Ultrasound Sequences.

Breast cancer needs to be detected early to reduce mortality rate. Ultrasound imaging (US) could significantly enhance diagnosing cases with dense breasts. Most of the existing computer-aided diagnosis (CAD) systems employ a single ultrasound image for the breast tumor to extract features to classify it as benign or malignant. However, the accuracy of such CAD system is limited due to the large tumor size and shape variation, irregular and ambiguous tumor boundaries, and low signal-to-noise ratio in ultrasound images due to their noisy nature and the significant similarity between normal and abnormal tissues. To handle these issues, we propose a deep-learning-based radiomics method based on breast US sequences in this paper. The proposed approach involves three main components: radiomic features extraction based on a deep learning network, so-called ConvNeXt, a malignancy score pooling mechanism, and visual interpretations. Specifically, we employ the ConvNeXt network, a deep convolutional neural network (CNN) trained using the vision transformer style. We also propose an efficient pooling mechanism to fuse the malignancy scores of each breast US sequence frame based on image-quality statistics. The ablation study and experimental results demonstrate that our method achieves competitive results compared to other CNN-based methods.

Fully Automated Breast Density Segmentation and Classification Using Deep Learning.

Breast density estimation with visual evaluation is still challenging due to low contrast and significant fluctuations in the mammograms' fatty tissue background. The primary key to breast density classification is to detect the dense tissues in the mammographic images correctly. Many methods have been proposed for breast density estimation; nevertheless, most of them are not fully automated. Besides, they have been badly affected by low signal-to-noise ratio and variability of density in appearance and texture. This study intends to develop a fully automated and digitalized breast tissue segmentation and classification using advanced deep learning techniques. The conditional Generative Adversarial Networks (cGAN) network is applied to segment the dense tissues in mammograms. To have a complete system for breast density classification, we propose a Convolutional Neural Network (CNN) to classify mammograms based on the standardization of Breast Imaging-Reporting and Data System (BI-RADS). The classification network is fed by the segmented masks of dense tissues generated by the cGAN network. For screening mammography, 410 images of 115 patients from the INbreast dataset were used. The proposed framework can segment the dense regions with an accuracy, Dice coefficient, Jaccard index of 98%, 88%, and 78%, respectively. Furthermore, we obtained precision, sensitivity, and specificity of 97.85%, 97.85%, and 99.28%, respectively, for breast density classification. This study's findings are promising and show that the proposed deep learning-based techniques can produce a clinically useful computer-aided tool for breast density analysis by digital mammography.

Temporal mammogram image registration using optimized curvilinear coordinates.

Registration of mammograms plays an important role in breast cancer computer-aided diagnosis systems. Radiologists usually compare mammogram images in order to detect abnormalities. The comparison of mammograms requires a registration between them. A temporal mammogram registration method is proposed in this paper. It is based on the curvilinear coordinates, which are utilized to cope both with global and local deformations in the breast area. Temporal mammogram pairs are used to validate the proposed method. After registration, the similarity between the mammograms is maximized, and the distance between manually defined landmarks is decreased. In addition, a thorough comparison with the state-of-the-art mammogram registration methods is performed to show its effectiveness.