An Enhanced Mask R-CNN Approach for Pulmonary Embolism Detection and Segmentation.

Pulmonary embolism (PE) refers to the occlusion of pulmonary arteries by blood clots, posing a mortality risk of approximately 30%. The detection of pulmonary embolism within segmental arteries presents greater challenges compared with larger arteries and is frequently overlooked. In this study, we developed a computational method to automatically identify pulmonary embolism within segmental arteries using computed tomography (CT) images. The system architecture incorporates an enhanced Mask R-CNN deep neural network trained on PE-containing images. This network accurately localizes pulmonary embolisms in CT images and effectively delineates their boundaries. This study involved creating a local data set and evaluating the model predictions against pulmonary embolisms manually identified by expert radiologists. The sensitivity, specificity, accuracy, Dice coefficient, and Jaccard index values were obtained as 96.2%, 93.4%, 96.%, 0.95, and 0.89, respectively. The enhanced Mask R-CNN model outperformed the traditional Mask R-CNN and U-Net models. This study underscores the influence of Mask R-CNN's loss function on model performance, providing a basis for the potential improvement of Mask R-CNN models for object detection and segmentation tasks in CT images.

Detection of microaneurysms using ant colony algorithm in the early diagnosis of diabetic retinopathy.

Microaneurysms are lesions in the shape of small circular dilations which result from thinning in peripheral retinal blood vessels due to diabetes and increasing intra-retinal blood pressure. Because it is considered as the most important clinical finding in the diagnosis of diabetic retinopathy, accurate detection of these lesions bear utmost importance in the early diagnosis of diabetic retinopathy. The present study aims to accurately, effectively and automatically detect microaneurysms which are difficult to detect in color fundus images in early stage. To this aim, ant colony algorithm, which is an important optimization method, was used instead of conventional image processing techniques. First, retinal vascular structure was extracted from color fundus images in Messidor and DiaretDB1 data sets. Afterwards, the segmentation of microaneurysms was effectively carried out using ant colony algorithm. The same procedure was also applied to five different image processing and clustering algorithms (watershed, random walker, k-means, maximum entropy and region growing) in order to compare the performance of the proposed method with other methods. Microaneurysm images manually detected by a specialist eye doctor were used to measure the performances of above-mentioned methods. The similarities among microaneurysms which were automatically and manually segmented were tested using Dice and Jaccard similarity index values. Dice index values obtained from the study vary between 0.52 and 0.98 in maximum entropy, 0.55 and 0.88 in watershed, 0.75 and 0.86 in region growing, 0.55 and 0.78 in k-means, and 0.66 and 0.83 in random walker, and 0.81 and 0.9 in ant colony. Similar performance values were also obtained in Jaccard index. The results show that different performances were observed in the conventional segmentation of microaneurysms depending on the image quality. On the other hand, the ant colony based method proposed in this paper displays a more stabilized and higher performance irrespective of image contrast. Therefore, it is evident that the proposed method successfully detects microaneurysms even in low quality images, thus helping specialists diagnose them in an easier way.

Voice Acoustic Analysis of Pediatric Vocal Nodule Patients Using Ratios Calculated With Biomedical Image Segmentation.

OBJECTIVE: The aim of this study was to determine nodules using newly developed software with a computer-assisted visual process technique for the calculation of size. The effects of the ratios of nodule base and width were evaluated with voice acoustic analysis. METHODS: A total of 72 patients with pediatric vocal nodule were evaluated. Nodules were marked with the ImageJ News program on photographs obtained from the video recordings in the videostroboscopic examination and classified according to the Shah et al scale. Segmentation was applied automatically. The ratios were taken as base of nodule/width and base of nodule/vocal cord. In the voice acoustic analysis, basic frequencies (mean F0), jitter (local %), shimmer (local %), and harmonicity (mean harmonics-to-noise [mean HNR]) were evaluated. RESULTS: A statistically significant negative correlation was determined between the mean F0 value and the nodule base/width ratio (P = 0.042, r = -0.240). A negative statistically significant relationship was determined between jitter (%) and vocal nodule base/width (P = 0.009, r = -0.305). A statistically significant positive correlation was determined between mean HNR and vocal nodule base/width (P = 0.034, r = 0.324). In discriminant analysis, correct classification of the Shah et al scale degrees of the classifying variables was 73.6%. CONCLUSION: Through collaboration with the biomedical engineering department, the results of this study determined new ratios in patients with pediatric vocal nodule. In voice acoustic analysis, the mean F0 was more affected by the width of the nodule, mean HNR was affected by the length of the base of the nodule, and jitter (%) was affected by the width of the nodule.