Optical Coherence Tomography Volume Data Research Articles

An Efficient OCT Fingerprint Antispoofing Method Based on ResMamba

Optical coherence tomography (OCT), known for its noncontact and 3D imaging capabilities, has found widespread application in fingerprint antispoofing detection. However, the existing methods rely heavily on single-frame B-scan images, underutilizing the 3D spatial information inherent in OCT volume data. High computational costs further limit its practical applications. Thus, this study proposes an efficient fingerprint antispoofing method which leverages the spatial continuity of OCT volume data to enhance both the accuracy and computational efficiency. Using an OCT system, we collected 320 real fingerprints and 320 spoofed fingerprints. Then, to distinguish between genuine and spoofed fingerprints, we developed the proposed ResMamba model, which is based on an enhanced 3D convolutional network integrated with a state space model (SSM). We extracted regions of interest (ROIs) from B-scan images and segmented them into volume slices for training and classification. The experimental results demonstrate that ResMamba achieved a 0.56% error rate (ERR) and 99.22% area under the curve (AUC), with an inference time of just 11 ms. Furthermore, compared to the existing models, ResMamba effectively balances its accuracy, inference speed, and model size. Ablation studies confirm that integrating the SIC module enhances the model’s robustness. Overall, ResMamba offers an efficient and reliable fingerprint antispoofing solution, outperforming the traditional methods in terms of its accuracy and performance.

Sweat Gland Extraction From Optical Coherence Tomography Using Convolutional Neural Network

As the Level 3 features of fingerprint, sweat pores have attracted attention in the field of fingerprint recognition and have been successfully applied to automatic fingerprint recognition systems. Traditional surface sweat pores become unclear or disappeared when the finger is contaminated, dried, or damaged. These unstable factors create major challenges in collecting sweat pores. Subcutaneous sweat glands belong to the internal tissues of fingers, which are stable and immune to external disturbances. This study investigated the extraction of subcutaneous sweat glands from fingertip volume data collected by optical coherence tomography (OCT). First, an improved multitask V-Net is proposed to extract subcutaneous sweat glands from OCT volume data. The network has an encoding path for features extraction and two decoding paths for extracting sweat gland boundaries and regions, respectively. The multitask scheme is designed to enhance the boundary and shape information of sweat glands and to prevent false extraction caused by interference from other tissues. Second, three mapping methods are proposed to address the problem of different spatial orientations of sweat glands. These three mapping methods, namely, global direct mapping (GDM), local direct mapping (LDM), and cylindrical fitting mapping (CFM), are used to map sweat glands to the surface fingerprint. Experiments are conducted in terms of sweat gland extraction, mapping, and matching. The qualitative and quantitative results show that the proposed network for sweat glands extraction outperforms other methods and that the LDM and CFM methods derive more accurate positions of sweat glands on the surface fingerprint than GDM. In the matching experiment, the equal error rate (EER) of dual-decoding V-Net (DDVN) reached 0.58%, which verified the recognition ability of sweat glands and the effectiveness of the proposed network.

Subcutaneous sweat pore estimation from optical coherence tomography

Abstract Sweat pore, one of the level 3 features of fingerprint, has attracted much attention in fingerprint recognition. Traditional sweat pores on surface fingerprint are unclear or blurred when fingers are stained or damaged. Subcutaneous sweat pores, as cross section of the sweat glands, are resistant to external interferences. With 3D fingertip information measured by optical coherence tomography (OCT), the subcutaneous sweat pore estimation from OCT volume data is investigated. First, an adaptive subcutaneous pore image reconstruction method is proposed. It utilizes the skin surface and viable epidermis junction as reference and realizes depth-adaptive pore image reconstruction. Second, a dilated U-Net combining the U-Net with dilated convolution is proposed for subcutaneous sweat pore extraction, which can prevent information loss of sweat pores caused by downsampling. To the best knowledge, it is the first time that subcutaneous sweat pore extraction is investigated and proposed. Experiments on subcutaneous pore image reconstruction and sweat pore extraction are both conducted. The qualitative and quantitative results show that the proposed adaptive method performs better in subcutaneous pore image reconstruction compared with the fix-depth method, and the dilated U-Net outperforms other methods on subcutaneous sweat pore extraction.

Surface and Internal Fingerprint Reconstruction From Optical Coherence Tomography Through Convolutional Neural Network

Optical coherence tomography (OCT), as a non-destructive and high-resolution fingerprint acquisition technology, is robust against poor skin conditions and resistant to spoof attacks. It measures fingertip information on and beneath skin as 3D volume data, containing the surface fingerprint, internal fingerprint and sweat glands. Various methods have been proposed to extract internal fingerprints, which ignore the inter-slice dependence and often require manually selected parameters. In this article, a modified U-Net that combines residual learning, bidirectional convolutional long short-term memory and hybrid dilated convolution (denoted as BCL-U Net) for OCT volume data segmentation and two fingerprint reconstruction approaches are proposed. To the best of our knowledge, it is the first time that simultaneous and automatic extraction is performed for surface fingerprint, internal fingerprint and sweat gland. The proposed BCL-U Net utilizes the spatial dependence in OCT volume data and deals with segmentation of objects with diverse sizes to achieve accurate extraction. Comparisons have been performed to demonstrate the advantages of the proposed method. A thorough evaluation of the recognition abilities of internal and surface fingerprints is conducted using a dataset significantly larger than previous studies. Four databases containing internal and surface fingerprints are generated from 1572 OCT volume data by the proposed method. The internal fingerprint matching experiment has achieved a lowest equal error rate (EER) of 0.95%. Mixed internal and surface fingerprint matching experiment is also performed and achieves an EER of 3.67%, verifying the consistency of the internal and surface fingerprints. The matching experiments for fingers under poor skin conditions show a 2.47% EER of internal fingerprints that is much lower than that of surface fingerprints, which proves the advantage of internal fingerprints and indicates the potential of the internal fingerprints to supplement or replace the surface fingerprints for some specific applications.

Deviation Maps for Understanding Thickness Changes of Inner Retinal Layers in Children with Type 1 Diabetes Mellitus

ABSTRACTPurpose: To analyze the use of deviation maps (DevMs) to understand thickness changes of inner retinal layers in optical coherence tomography (OCT) data. To test a new visual analytics (VA) method with reduced complexity of OCT data analysis by comparing the layer thickness of children with type 1 diabetes mellitus (T1DM) to matched controls.Methods: OCT was performed on unilateral eyes of 26 children with T1DM without diabetic retinopathy and 29 healthy children to obtain macular volume scans. Subsequently, segmented inner retinal layers were analyzed using VA. Deviation maps were generated to readily visualize thickness differences between both groups and to investigate thickness changes of individual patients in relation to the control group.Results: In DevMs of the patient group, the total retina (TR) demonstrated localized, irregular areas of thinning (mean ± standard deviation) involving foveal center, inner macula, and inferior-nasal outer macula (−9.31 ± 1.73 µm; p < 0.05). Similarly, retinal nerve fiber layer showed continuous and localized areas of thinning in both inner and outer macula, extending nasally (−5.45 ± 4.31 µm; p < 0.05). In DevMs of individual patients, the TR and inner retinal layers revealed remarkable changes in thickness that were present between patients at both late and early stages of diabetes.Conclusion: The VA method simplifies the in-depth analysis of OCT volume data from different groups and is effective in detecting retinal thickness changes in children with diabetes. It can be easily adopted in a clinical set-up and intuitively used in complex multidisciplinary studies.

Increased Inner Retinal Layer Reflectivity in Eyes With Acute CRVO Correlates With Worse Visual Outcomes at 12 Months.

To determine if inner retinal layer reflectivity in eyes with acute central retinal vein occlusion (CRVO) correlates with visual acuity at 12 months. Macular optical coherence tomography (OCT) scans were obtained from 22 eyes of 22 patients with acute CRVO. Optical intensity ratios (OIRs), defined as the mean OCT reflectivity of the inner retinal layers normalized to the mean reflectivity of the RPE, were measured from the presenting and 1-month OCT image by both manual measurements of grayscale B-scans and custom algorithmic measurement of raw OCT volume data. OIRs were assessed for association with final visual outcome. Cohort subgroup division for analysis was determined statistically. Eyes with poorer final visual acuity (≥20/70) at 1 year were more likely to have a higher ganglion cell layer OIR than eyes with better final visual acuity (<20/70) at 1 month (manually: 0.591 to 0.735, P = 0.006, algorithmically: 0.663 to 0.799, P = 0.014). At 1 month, eyes with a poorer final visual acuity demonstrated a higher variance of OIR measurements (algorithmically: 0.087 vs. 0.160, P = 0.002) per scan than eyes with better final visual acuity. In acute CRVO, ganglion cell layer changes at 1 month, including increased reflectivity and increased heterogeneity of reflectivity signal as expressed as OIR and OIR variance, were associated with a poorer visual prognosis at 1 year. Technique calibration with larger sample sizes and automated integration into OCT platforms will be necessary to determine if OIR can be a clinically useful prognostic tool.

A deep learning approach for pose estimation from volumetric OCT data.

Tracking the pose of instruments is a central problem in image-guided surgery. For microscopic scenarios, optical coherence tomography (OCT) is increasingly used as an imaging modality. OCT is suitable for accurate pose estimation due to its micrometer range resolution and volumetric field of view. However, OCT image processing is challenging due to speckle noise and reflection artifacts in addition to the images' 3D nature. We address pose estimation from OCT volume data with a new deep learning-based tracking framework. For this purpose, we design a new 3D convolutional neural network (CNN) architecture to directly predict the 6D pose of a small marker geometry from OCT volumes. We use a hexapod robot to automatically acquire labeled data points which we use to train 3D CNN architectures for multi-output regression. We use this setup to provide an in-depth analysis on deep learning-based pose estimation from volumes. Specifically, we demonstrate that exploiting volume information for pose estimation yields higher accuracy than relying on 2D representations with depth information. Supporting this observation, we provide quantitative and qualitative results that 3D CNNs effectively exploit the depth structure of marker objects. Regarding the deep learning aspect, we present efficient design principles for 3D CNNs, making use of insights from the 2D deep learning community. In particular, we present Inception3D as a new architecture which performs best for our application. We show that our deep learning approach reaches errors at our ground-truth label's resolution. We achieve a mean average error of 14.89 ± 9.3µm and 0.096 ± 0.072° for position and orientation learning, respectively.

Performance evaluation of automated segmentation software on optical coherence tomography volume data.

Over the past two decades a significant number of OCT segmentation approaches have been proposed in the literature. Each methodology has been conceived for and/or evaluated using specific datasets that do not reflect the complexities of the majority of widely available retinal features observed in clinical settings. In addition, there does not exist an appropriate OCT dataset with ground truth that reflects the realities of everyday retinal features observed in clinical settings. While the need for unbiased performance evaluation of automated segmentation algorithms is obvious, the validation process of segmentation algorithms have been usually performed by comparing with manual labelings from each study and there has been a lack of common ground truth. Therefore, a performance comparison of different algorithms using the same ground truth has never been performed. This paper reviews research-oriented tools for automated segmentation of the retinal tissue on OCT images. It also evaluates and compares the performance of these software tools with a common ground truth.

Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region.

Optical coherence tomography (OCT) is a high speed, high resolution and non-invasive imaging modality that enables the capturing of the 3D structure of the retina. The fast and automatic analysis of 3D volume OCT data is crucial taking into account the increased amount of patient-specific 3D imaging data. In this work, we have developed an automatic algorithm, OCTRIMA 3D (OCT Retinal IMage Analysis 3D), that could segment OCT volume data in the macular region fast and accurately. The proposed method is implemented using the shortest-path based graph search, which detects the retinal boundaries by searching the shortest-path between two end nodes using Dijkstra’s algorithm. Additional techniques, such as inter-frame flattening, inter-frame search region refinement, masking and biasing were introduced to exploit the spatial dependency between adjacent frames for the reduction of the processing time. Our segmentation algorithm was evaluated by comparing with the manual labelings and three state of the art graph-based segmentation methods. The processing time for the whole OCT volume of 496×644×51 voxels (captured by Spectralis SD-OCT) was 26.15 seconds which is at least a 2-8-fold increase in speed compared to other, similar reference algorithms used in the comparisons. The average unsigned error was about 1 pixel (∼ 4 microns), which was also lower compared to the reference algorithms. We believe that OCTRIMA 3D is a leap forward towards achieving reliable, real-time analysis of 3D OCT retinal data.

Adaptive Speckle Reduction in OCT Volume Data Based on Block-Matching and 3-D Filtering

An adaptive speckle denoising method called Volume-BM3D is developed for optical coherence tomography (OCT) volume data processing, including an adaptive noise level estimation algorithm and a denoising strategy based on block-matching and filtering in a highly sparse local 3-D transform domain. Noise level estimation, an important step for denoising, is obtained from estimating signal fluctuations among neighboring A-Scans. Unlike 2-D denoising approaches, the Volume-BM3D considers the similarities of tissue structures either in a single 2-D image or in a volume data simultaneously. It not only effectively suppresses speckle noise, but also improves the visualization of small morphological features, such as sweat glands in finger images. Application of this proposed method to the OCT volume data of a human finger tip shows an 18.4-dB signal-to-noise ratio improvement in speckle noise reduction with little edge blurring. It outperforms other filtering methods in suppressing speckle noises and revealing attenuated subtle features.

Alignment of 3-D Optical Coherence Tomography Scans to Correct Eye Movement Using a Particle Filtering

Eye movement artifacts occurring during 3-D optical coherence tomography (OCT) scanning is a well-recognized problem that may adversely affect image analysis and interpretation. A particle filtering algorithm is presented in this paper to correct motion in a 3-D dataset by considering eye movement as a target tracking problem in a dynamic system. The proposed particle filtering algorithm is an independent 3-D alignment approach, which does not rely on any reference image. 3-D OCT data is considered as a dynamic system, while the location of each A-scan is represented by the state space. A particle set is used to approximate the probability density of the state in the dynamic system. The state of the system is updated frame by frame to detect A-scan movement. The proposed method was applied on both simulated data for objective evaluation and experimental data for subjective evaluation. The sensitivity and specificity of the x-movement detection were 98.85% and 99.43%, respectively, in the simulated data. For the experimental data (74 3-D OCT images), all the images were improved after z-alignment, while 81.1% images were improved after x-alignment. The proposed algorithm is an efficient way to align 3-D OCT volume data and correct the eye movement without using references.

3D OCT eye movement correction based on particle filtering.

Three-dimensional optical coherence tomography (OCT) is a new ophthalmic imaging technique offering more detailed quantitative analysis of the retinal structure. Eye movement during 3D OCT scanning, however, creates significant spatial distortions that may adversely affect image interpretation and analysis. Current software solutions must use additional reference images or B-scans to correct eye movement in a certain direction. The proposed particle filtering algorithm is an independent 3D alignment approach, which does not rely on any reference image. 3D OCT data is considered as a dynamic system, while location of A-scan is represented by the state space. A particle set is generated to approximate the probability density of the state. The state of the system is updated frame by frame to detect A-scan movement. Seventy-four 3D OCT images with eye movement were tested and subjectively evaluated by comparing them with the original images. All the images were improved after z-alignment, while 81.1% images were improved after x-alignment. The proposed algorithm is an efficient way to align 3D OCT volume data and correct the eye movement without using references.

Multifrequency swept common-path en-face OCT for wide-field measurement of interior surface vibrations in thick biological tissues.

Microvibrations that occur in bio-tissues are considered to play pivotal roles in organ function; however techniques for their measurement have remained underdeveloped. To address this issue, in the present study we have developed a novel optical coherence tomography (OCT) method that utilizes multifrequency swept interferometry. The OCT volume data can be acquired by sweeping the multifrequency modes produced by combining a tunable Fabry-Perot filter and an 840 nm super-luminescent diode with a bandwidth of 160 nm. The system employing the wide-field heterodyne method does not require mechanical scanning probes, which are usually incorporated in conventional Doppler OCTs and heterodyne-type interferometers. These arrangements allow obtaining not only 3D tomographic images but also various vibration parameters such as spatial amplitude, phase, and frequency, with high temporal and transverse resolutions over a wide field. Indeed, our OCT achieved the axial resolution of ~2.5 μm when scanning the surface of a glass plate. Moreover, when examining a mechanically resonant multilayered bio-tissue in full-field configuration, we captured 22 nm vibrations of its internal surfaces at 1 kHz by reconstructing temporal phase variations. This so-called "multifrequency swept common-path en-face OCT" can be applied for measuring microdynamics of a variety of biological samples, thus contributing to the progress in life sciences research.