Evaluation of Publicly Available Blood Vessel Segmentation Methods for Retinal Images

Retinal blood vessel structure is an important indicator of disorders related to diseases, which has motivated the development of various image segmentation methods for the blood vessels. In this study, two supervised and two unsupervised retinal blood vessel segmentation methods are quantitatively compared by using five publicly available databases with the ground truth for the vessels. The parameters of each method were optimized for each database with the motivation to achieve good segmentation performance for the comparison and study the importance of proper selection of parameter values. The results show that parameter optimization does not significantly improve the segmentation performance of the methods when the original data is used. However, the methods’ performance for new data differs significantly. Based on the comparison, Soares method as a supervised approach provided the highest overall accuracy and, thus, the best generalisability. Bankhead and Nguyen methods’ performance were close to each other: Bankhead performed better with ARIADB and STARE, whereas Nguyen was better with DRIVE. Sofka method is available only as an executable and its performance matched the others only with ARIADB

Oral and Dental Spectral Image Database—ODSI-DB

The most common imaging methods used in dentistry are X-ray imaging and RGB color photography. However, both imaging methods provide only a limited amount of information on the wavelength-dependent optical properties of the hard and soft tissues in the mouth. Spectral imaging, on the other hand, provides significantly more information on the medically relevant dental and oral features (e.g. caries, calculus, and gingivitis). Due to this, we constructed a spectral imaging setup and acquired 316 oral and dental reflectance spectral images, 215 of which are annotated by medical experts, of 30 human test subjects. Spectral images of the subjects’ faces and other areas of interest were captured, along with other medically relevant information (e.g., pulse and blood pressure). We collected these oral, dental, and face spectral images, their annotations and metadata into a publicly available database that we describe in this paper. This oral and dental spectral image database (ODSI-DB) provides a vast amount of data that can be used for developing, e.g., pattern recognition and machine vision applications for dentistry

The Optimization of the Light-Source Spectrum Utilizing Neural Networks for Detecting Oral Lesions

Any change in the light-source spectrum modifies the color information of an object. The spectral distribution of the light source can be optimized to enhance specific details of the obtained images; thus, using information-enhanced images is expected to improve the image recognition performance via machine vision. However, no studies have applied light spectrum optimization to reduce the training loss in modern machine vision using deep learning. Therefore, we propose a method for optimizing the light-source spectrum to reduce the training loss using neural networks. A two-class classification of one-vs-rest among the classes, including enamel as a healthy condition and dental lesions, was performed to validate the proposed method. The proposed convolutional neural network-based model, which accepts a 5 × 5 small patch image, was compared with an alternating optimization scheme using a linear-support vector machine that optimizes classification weights and lighting weights separately. Furthermore, it was compared with the proposed neural network-based algorithm, which inputs a pixel and consists of fully connected layers. The results of the five-fold cross-validation revealed that, compared to the previous method, the proposed method improved the F1-score and was superior to the models that were using the immutable standard illuminant D65

Hyperspectral imaging in brain tumor surgery:evidence of machine learning-based performance

Abstract Background: Hyperspectral imaging (HSI) has the potential to enhance surgical tissue detection and diagnostics. Definite utilization of intraoperative HSI guidance demands validated machine learning and public datasets that currently do not exist. Moreover, current imaging conventions are dispersed, and evidence-based paradigms for neurosurgical HSI have not been declared. Methods: We presented the rationale and a detailed clinical paradigm for establishing microneurosurgical HSI guidance. In addition, a systematic literature review was conducted to summarize the current indications and performance of neurosurgical HSI systems, with an emphasis on machine learning-based methods. Results: The published data comprised a few case series or case reports aiming to classify tissues during glioma operations. For a multitissue classification problem, the highest overall accuracy of 80% was obtained using deep learning. Our HSI system was capable of intraoperative data acquisition and visualization with minimal disturbance to glioma surgery. Conclusions: In a limited number of publications, neurosurgical HSI has demonstrated unique capabilities in contrast to the established imaging techniques. Multidisciplinary work is required to establish communicable HSI standards and clinical impact. Our HSI paradigm endorses systematic intraoperative HSI data collection, which aims to facilitate the related standards, medical device regulations, and value-based medical imaging systems