SEEG study of a rare male temporal lobe epilepsy with orgasmic aura originating from the right amygdala.

As a primitive driving force for biological reproduction, sexual behavior (and its associated mechanisms) is extremely complex, and orgasm plays an essential role. The limbic system plays a very important role in regulating human sexual behavior. However, it is not clear which components of the limbic system are related to orgasm sensation. We studied a rare case of spontaneous orgasmic aura in a male patient with temporal lobe epilepsy. Stereoelectroencephalography (SEEG) revealed that the right amygdala was the origin of orgasmic aura. Surgical removal of the medial temporal lobe, including the right amygdala, completely eliminated the patient's seizures. This study demonstrates the critical role of the amygdala in human male orgasm.

ECG-Based Multiclass Arrhythmia Classification Using Beat-Level Fusion Network.

Cardiovascular disease (CVD) is one of the most severe diseases threatening human life. Electrocardiogram (ECG) is an effective way to detect CVD. In recent years, many methods have been proposed to detect arrhythmia using 12-lead ECG. In particular, deep learning methods have been proven to be effective and have been widely used. The attention mechanism has attracted extensive attention in many fields in a series of deep learning methods. Off-the-shelf solutions based on deep learning and attention mechanism for ECG classification mostly give weights to time points. None of the existing methods were considered using the attention mechanism dealing with ECG signals at the level of heartbeats. In this paper, we propose a beat-level fusion net (BLF-Net) for multiclass arrhythmia classification by assigning weights at the heartbeat level, according to the contribution of the heartbeat to diagnostic results. This algorithm consists of three steps: (1) segmenting the long ECG signal into short beats; (2) using a neural network to extract features from heartbeats; and (3) assigning weights to features extracted from heartbeats using an attention mechanism. We test our algorithm on the PTB-XL database and have superiority over state-of-the-art performance on six classification tasks. Besides, the principle of this architecture is clarified by visualizing the weight of the attention mechanism. The proposed BLF-Net is shown to be useful and automatically provides an effective network structure for arrhythmia classification, which is capable of aiding cardiologists in arrhythmia diagnosis.

A GAN Guided Parallel CNN and Transformer Network for EEG Denoising.

Electroencephalography (EEG) signals are often contaminated with various physiological artifacts, seriously affecting the quality of subsequent analysis. Therefore, removing artifacts is an essential step in practice. As of now, deep learning-based EEG denoising methods have exhibited unique advantages over traditional methods. However, they still suffer from the following limitations. The existing structure designs have not fully taken into account the temporal characteristics of artifacts. Meanwhile, the existing training strategies usually ignore the holistic consistency between denoised EEG signals and authentic clean ones. To address these issues, we propose a GAN guided parallel CNN and transformer network, named GCTNet. The generator contains parallel CNN blocks and transformer blocks to respectively capture local and global temporal dependencies. Then, a discriminator is employed to detect and correct the holistic inconsistencies between clean and denoised EEG signals. We evaluate the proposed network on both semi-simulated and real data. Extensive experimental results demonstrate that GCTNet significantly outperforms state-of-the-art networks in various artifact removal tasks, as evidenced by its superior objective evaluation metrics. For example, in the task of removing electromyography artifacts, GCTNet achieves 11.15% reduction in RRMSE and 9.81% improvement in SNR over other methods, highlighting the potential of the proposed method as a promising solution for EEG signals in practical applications.

A Self-Interpretable Deep Learning Model for Seizure Prediction Using a Multi-Scale Prototypical Part Network.

The epileptic seizure prediction (ESP) method aims to timely forecast the occurrence of seizures, which is crucial to improving patients' quality of life. Many deep learning-based methods have been developed to tackle this issue and achieve significant progress in recent years. However, the "black-box" nature of deep learning models makes the clinician mistrust the prediction results, severely limiting its clinical application. For this purpose, in this study, we propose a self-interpretable deep learning model for patient-specific epileptic seizure prediction: Multi-Scale Prototypical Part Network (MSPPNet). This model attempts to measure the similarity between the inputs and prototypes (learned during training) as evidence to make final predictions, which could provide a transparent reasoning process and decision basis (e.g., significant prototypes for inputs and corresponding similarity score). Furthermore, we assign different sizes to the prototypes in latent space to capture the multi-scale features of EEG signals. To the best of our knowledge, this is the first study that develops a self-interpretable deep learning model for seizure prediction, other than the existing post hoc interpretation studies. Our proposed model is evaluated on two public epileptic EEG datasets (CHB-MIT: 16 patients with a total of 85 seizures, Kaggle: 5 dogs with a total of 42 seizures), with a sensitivity of 93.8% and a false prediction rate of 0.054/h in the CHB-MIT dataset and a sensitivity of 88.6% and a false prediction rate of 0.146/h in the Kaggle dataset, achieving the current state-of-the-art performance with self-interpretable evidence.

Stereoelectroencephalography-based research on the value of drug-resistant temporal lobe epilepsy auras: A retrospective single-center study.

PURPOSE: To explore the localization value of drug-resistant temporal lobe epilepsy (TLE) aura for preoperative evaluation, based on stereoelectroencephalography (SEEG), and its prognostic value on the surgical outcome. METHODS: The data of patients with drug-resistant TLE who had SEEG electrodes implanted during preoperative evaluation at the First Affiliated Hospital of the University of Science and Technology of China (Hefei, China) were retrospectively analyzed. The patients were divided into aura-positive and aura-negative groups according to the presence of aura in seizures. To explore the clinical features of aura, we evaluated the localizing and prognostic values of aura for the outcome of anterior temporal lobectomy based on SEEG. RESULTS: Among forty patients, twenty-seven patients were in the aura-positive group and ten (25.0%) patients had multiple auras. The most common TLE aura was abdominal aura [thirteen (34.2%) patients]. The postoperative seizure frequency was significantly reduced in the preoperative aura-positive patients compared to the preoperative aura-negative patients (P = 0.011). Patients with abdominal (P = 0.029) and single (P = 0.036) auras had better surgical prognoses than aura-negative patients. In the preoperative evaluation, aura-positive patients had a better surgical outcome if the laterality of positron emission tomography-computed tomography (PET-CT) hypometabolism was concordant with the epileptogenic focus identified with SEEG (P = 0.031). A good postoperative epileptic outcome in aura-positive patients was observed among those with hippocampal sclerotic medial temporal lobe epilepsy (P = 0.025). CONCLUSION: Epileptic aura is valuable for the localization of the epileptogenic focus. Abdominal aura and single aura were good predictors of better surgical outcomes. Among patients with a preoperative diagnosis of hippocampal sclerosis or with laterality of PET-CT hypometabolism concordant with the epileptogenic focus identified using SEEG, those with aura are likely to benefit from surgery.

EEG-Based Seizure Prediction via Model Uncertainty Learning.

Deep neural networks (DNNs) have the powerful ability to automatically extract efficient features, which makes them prominent in electroencephalogram (EEG) based seizure prediction tasks. However, current research in this field cannot take the model uncertainty into account, causing the prediction less credible. To this end, we introduce a novel end-to-end patient-specific seizure prediction framework via model uncertainty learning. Specifically, we propose a reparameterized EEG-based lightweight CNN architecture and a modified Monte Carlo dropout (RepNet-MMCD) strategy to improve the reliability of the DNNs-based model. In RepNet, we obtain multi-scale feature representations by applying depthwise separable convolutions of different kernels. After training, depthwise convolutions with different scales are equivalently converted into a single convolution layer, which can greatly reduce computational budgets without losing model performance. In addition, we propose a modified Monte Carlo (MMCD) strategy, leveraging the samples-based temporal information in EEG signals to simulate the Monte Carlo dropout sampling. Sensitivity, false-positive rate (FPR), and area under curve (AUC) of the proposed RepNet-MMCD achieve 93.1%, 0.033/h, 0.950 and 81.6%, 0.056/h, 0.903 on two public datasets, respectively. We further extend the MMCD strategy to the other baseline methods, which can improve the performance of seizure prediction by a clear margin.

A general sample-weighted framework for epileptic seizure prediction.

OBJECTIVE: Effective epileptic seizure prediction can make the patients know the onset of the seizure in advance to take timely preventive measures. Many studies based on machine learning methods have been proposed to tackle this problem and achieve significant progress in recent years. However, most studies treat each EEG training sample's contribution to the model as equal, while different samples have different predictive effects on epileptic seizures (e.g., preictal samples from different times). To this end, in this paper, we propose a general sample-weighted framework for patient-specific epileptic seizure prediction. METHODS: Specifically, we define the mapping from the sample weights of training sets to the performance of the validation sets as the fitness function to be optimized. Then, the genetic algorithm is employed to optimize this fitness function and obtain the optimal sample weights. Finally, we obtain the final model by using the training sets with optimized sample weights. RESULTS: To evaluate the effectiveness of our framework, we conduct extensive experiments on both traditional machine learning methods and prevalent deep learning methods. Our framework can significantly improve performance based on these methods. Among them, our framework based on Transformer achieves an average sensitivity of 94.6%, an average false prediction rate of 0.06/h, and an average AUC of 0.939 in 12 pediatric patients from the CHB-MIT database with the leave-one-out method, which outperforms the state-of-the-art methods. CONCLUSION: This study provides new insights into the field of epileptic seizure prediction by considering the discrepancies between EEG samples. Moreover, we develop a general sample-weighted framework, which applies to almost all classical classification methods and can significantly improve performance based on these methods.

Brain hemispheres with right temporal lobe damage swap dominance in early auditory processing of lexical tones.

Labor division of the two brain hemispheres refers to the dominant processing of input information on one side of the brain. At an early stage, or a preattentive stage, the right brain hemisphere is shown to dominate the auditory processing of tones, including lexical tones. However, little is known about the influence of brain damage on the labor division of the brain hemispheres for the auditory processing of linguistic tones. Here, we demonstrate swapped dominance of brain hemispheres at the preattentive stage of auditory processing of Chinese lexical tones after a stroke in the right temporal lobe (RTL). In this study, we frequently presented lexical tones to a group of patients with a stroke in the RTL and infrequently varied the tones to create an auditory contrast. The contrast evoked a mismatch negativity response, which indexes auditory processing at the preattentive stage. In the participants with a stroke in the RTL, the mismatch negativity response was lateralized to the left side, in contrast to the right lateralization pattern in the control participants. The swapped dominance of brain hemispheres indicates that the RTL is a core area for early-stage auditory tonal processing. Our study indicates the necessity of rehabilitating tonal processing functions for tonal language speakers who suffer an RTL injury.

Transient Motion Classification Through Turbid Volumes via Parallelized Single-Photon Detection and Deep Contrastive Embedding.

Fast noninvasive probing of spatially varying decorrelating events, such as cerebral blood flow beneath the human skull, is an essential task in various scientific and clinical settings. One of the primary optical techniques used is diffuse correlation spectroscopy (DCS), whose classical implementation uses a single or few single-photon detectors, resulting in poor spatial localization accuracy and relatively low temporal resolution. Here, we propose a technique termed C lassifying R apid decorrelation E vents via P arallelized single photon d E tection (CREPE), a new form of DCS that can probe and classify different decorrelating movements hidden underneath turbid volume with high sensitivity using parallelized speckle detection from a 32 × 32 pixel SPAD array. We evaluate our setup by classifying different spatiotemporal-decorrelating patterns hidden beneath a 5 mm tissue-like phantom made with rapidly decorrelating dynamic scattering media. Twelve multi-mode fibers are used to collect scattered light from different positions on the surface of the tissue phantom. To validate our setup, we generate perturbed decorrelation patterns by both a digital micromirror device (DMD) modulated at multi-kilo-hertz rates, as well as a vessel phantom containing flowing fluid. Along with a deep contrastive learning algorithm that outperforms classic unsupervised learning methods, we demonstrate our approach can accurately detect and classify different transient decorrelation events (happening in 0.1-0.4 s) underneath turbid scattering media, without any data labeling. This has the potential to be applied to non-invasively monitor deep tissue motion patterns, for example identifying normal or abnormal cerebral blood flow events, at multi-Hertz rates within a compact and static detection probe.

Patient-Specific Seizure Prediction via Adder Network and Supervised Contrastive Learning.

Deep learning (DL) methods have been widely used in the field of seizure prediction from electroencephalogram (EEG) in recent years. However, DL methods usually have numerous multiplication operations resulting in high computational complexity. In addtion, most of the current approaches in this field focus on designing models with special architectures to learn representations, ignoring the use of intrinsic patterns in the data. In this study, we propose a simple and effective end-to-end adder network and supervised contrastive learning (AddNet-SCL). The method uses addition instead of the massive multiplication in the convolution process to reduce the computational cost. Besides, contrastive learning is employed to effectively use label information, points of the same class are clustered together in the projection space, and points of different class are pushed apart at the same time. Moreover, the proposed model is trained by combining the supervised contrastive loss from the projection layer and the cross-entropy loss from the classification layer. Since the adder networks uses the l1 -norm distance as the similarity measure between the input feature and the filters, the gradient function of the network changes, an adaptive learning rate strategy is employed to ensure the convergence of AddNet-CL. Experimental results show that the proposed method achieves 94.9% sensitivity, an area under curve (AUC) of 94.2%, and a false positive rate of (FPR) 0.077/h on 19 patients in the CHB-MIT database and 89.1% sensitivity, an AUC of 83.1%, and an FPR of 0.120/h in the Kaggle database. Competitive results show that this method has broad prospects in clinical practice.

Imaging Dynamics Beneath Turbid Media via Parallelized Single-Photon Detection.

Noninvasive optical imaging through dynamic scattering media has numerous important biomedical applications but still remains a challenging task. While standard diffuse imaging methods measure optical absorption or fluorescent emission, it is also well-established that the temporal correlation of scattered coherent light diffuses through tissue much like optical intensity. Few works to date, however, have aimed to experimentally measure and process such temporal correlation data to demonstrate deep-tissue video reconstruction of decorrelation dynamics. In this work, a single-photon avalanche diode array camera is utilized to simultaneously monitor the temporal dynamics of speckle fluctuations at the single-photon level from 12 different phantom tissue surface locations delivered via a customized fiber bundle array. Then a deep neural network is applied to convert the acquired single-photon measurements into video of scattering dynamics beneath rapidly decorrelating tissue phantoms. The ability to reconstruct images of transient (0.1-0.4 s) dynamic events occurring up to 8 mm beneath a decorrelating tissue phantom with millimeter-scale resolution is demonstrated, and it is highlighted how the model can flexibly extend to monitor flow speed within buried phantom vessels.

Clinical, Radiological, Pathological Features and Seizure Outcome With Surgical Management of Polymorphous Low-Grade Neuroepithelial Tumor of the Young Associated With Epilepsy.

Objective: Polymorphous low-grade neuroepithelial tumor of the young (PLNTY) is a novel distinct epileptogenic neoplasm, and its clinical, imaging, histopathological, and molecular features were already known in the existing literature. We aimed to analyze the surgical management of PLNTY combined with these known characteristics. Methods: Eight patients underwent surgical treatment in our center between December 2017 and December 2020, and the postoperative pathology was diagnosed as PLNTY. Their clinical data, imaging, pathological, molecular characteristics, and seizure outcome were retrospectively analyzed. Follow-up evaluations and a literature review were performed. Results: The 8 patients included 1 woman and 7 men, aged between 5 and 51 years old (mean = 31.6, median = 29). The preoperative symptoms of all 8 cases were seizures. Four tumors were situated in the temporal lobes, and one of the four extratemporal tumors was in the occipital lobe and three were in the frontal lobe. Enlarged and gross total resections were performed in 2 cases and the other 6 cases, respectively. All cases exhibited intense labeling of CD34, and absence of 1p/19q codeletion and IDH1 or IDH2 mutation. B-Raf proto-oncogene (BRAF) V600E mutation was presented in 4 (66.7%) of 6 detected cases. The postoperative seizure outcome of Engel class I was achieved in 6 cases (75%). Conclusion: PLNTY represents distinctive histologic, immunophenotypic and biomolecular features, and has high epileptogenicity. Early surgical intervention and enlarged resection of PLNTY associated with epilepsy will help to improve the postoperative seizure-free rate.

Video-rate high-precision time-frequency multiplexed 3D coherent ranging.

Frequency-modulated continuous wave (FMCW) light detection and ranging (LiDAR) is an emerging 3D ranging technology that offers high sensitivity and ranging precision. Due to the limited bandwidth of digitizers and the speed limitations of beam steering using mechanical scanners, meter-scale FMCW LiDAR systems typically suffer from a low 3D frame rate, which greatly restricts their applications in real-time imaging of dynamic scenes. In this work, we report a high-speed FMCW based 3D imaging system, combining a grating for beam steering with a compressed time-frequency analysis approach for depth retrieval. We thoroughly investigate the localization accuracy and precision of our system both theoretically and experimentally. Finally, we demonstrate 3D imaging results of multiple static and moving objects, including a flexing human hand. The demonstrated technique achieves submillimeter localization accuracy over a tens-of-centimeter imaging range with an overall depth voxel acquisition rate of 7.6 MHz, enabling densely sampled 3D imaging at video rate.

Pediatric Seizure Prediction in Scalp EEG Using a Multi-Scale Neural Network With Dilated Convolutions.

Objective: Epileptic seizure prediction based on scalp electroencephalogram (EEG) is of great significance for improving the quality of life of patients with epilepsy. In recent years, a number of studies based on deep learning methods have been proposed to address this issue and achieve excellent performance. However, most studies on epileptic seizure prediction by EEG fail to take full advantage of temporal-spatial multi-scale features of EEG signals, while EEG signals carry information in multiple temporal and spatial scales. To this end, in this study, we proposed an end-to-end framework by using a temporal-spatial multi-scale convolutional neural network with dilated convolutions for patient-specific seizure prediction. Methods: Specifically, the model divides the EEG processing pipeline into two stages: the temporal multi-scale stage and the spatial multi-scale stage. In each stage, we firstly extract the multi-scale features along the corresponding dimension. A dilated convolution block is then conducted on these features to expand our model's receptive fields further and systematically aggregate global information. Furthermore, we adopt a feature-weighted fusion strategy based on an attention mechanism to achieve better feature fusion and eliminate redundancy in the dilated convolution block. Results: The proposed model obtains an average sensitivity of 93.3%, an average false prediction rate of 0.007 per hour, and an average proportion of time-in-warning of 6.3% testing in 16 patients from the CHB-MIT dataset with the leave-one-out method. Conclusion: Our model achieves superior performance in comparison to state-of-the-art methods, providing a promising solution for EEG-based seizure prediction.

Semisupervised Seizure Prediction in Scalp EEG Using Consistency Regularization.

Early prediction of epilepsy seizures can warn the patients to take precautions and improve their lives significantly. In recent years, deep learning has become increasingly predominant in seizure prediction. However, existing deep learning-based approaches in this field require a great deal of labeled data to guarantee performance. At the same time, labeling EEG signals does require the expertise of an experienced pathologist and is incredibly time-consuming. To address this issue, we propose a novel Consistency-based Semisupervised Seizure Prediction Model (CSSPM), where only a fraction of training data is labeled. Our method is based on the principle of consistency regularization, which underlines that a robust model should maintain consistent results for the same input under extra perturbations. Specifically, by using stochastic augmentation and dropout, we consider the entire neural network as a stochastic model and apply a consistency constraint to penalize the difference between the current prediction and previous predictions. In this way, unlabeled data could be fully utilized to improve the decision boundary and enhance prediction performance. Compared with existing studies requiring all training data to be labeled, the proposed method only needs a small portion of data to be labeled while still achieving satisfactory results. Our method provides a promising solution to alleviate the labeling cost for real-world applications.

Surgical resection of dysembryoplatic neuroepithelioma tumor associated with epilepsy based on imaging classification.

OBJECTIVE: Dysembryoplastic neuroepithelioma tumors (DNETs) are rare glioneuronal tumors usually present with partial epilepsy. We analyzed the surgical curative effect of DNETs based on imaging classification. METHODS: The clinical, neuroimaging, seizure history, neuropathological data, and other medical records of 21 cases of cerebral hemisphere DNETs were collected and analyzed retrospectively. According to the magnetic resonance imaging (MRI) classification of Chassoux, these cases were divided into 8 cases of type I (thylakoid type), 6 cases of type II (nodular type), and 7 cases of type III (dysplasia). All patients received detailed preoperative evaluation and underwent surgical treatment. We statistically compared the postoperative seizure outcome of different DNET MRI types by Engel classification. RESULTS: All tumors were surgically removed and pathologically diagnosed as DNETs. The follow-up period was 5-68 months Engel class I outcome was achieved in all type I cases, 3 (50%) type II cases, and 3 (42.9%) type III cases. The postoperative seizure outcome of MRI type I was better than that of type II and III. CONCLUSION: Based on the MRI classification of DNET by Chassoux, the postoperative epilepsy control of type I is better than that of type II and type III, which may be related to the residual FCD around the tumor of type II and type III. Thus, the MRI classification of DNET can contribute to the preoperative design of the resection plan. Total resection of type I and extended resection of type II, as well as type III, will help to improve the postoperative seizure-free rate in DNET.

Computational 3D microscopy with optical coherence refraction tomography.

Optical coherence tomography (OCT) has seen widespread success as an in vivo clinical diagnostic 3D imaging modality, impacting areas including ophthalmology, cardiology, and gastroenterology. Despite its many advantages, such as high sensitivity, speed, and depth penetration, OCT suffers from several shortcomings that ultimately limit its utility as a 3D microscopy tool, such as its pervasive coherent speckle noise and poor lateral resolution required to maintain millimeter-scale imaging depths. Here, we present 3D optical coherence refraction tomography (OCRT), a computational extension of OCT which synthesizes an incoherent contrast mechanism by combining multiple OCT volumes, acquired across two rotation axes, to form a resolution-enhanced, speckle-reduced, refraction-corrected 3D reconstruction. Our label-free computational 3D microscope features a novel optical design incorporating a parabolic mirror to enable the capture of 5D plenoptic datasets, consisting of millimetric 3D fields of view over up to ±75° without moving the sample. We demonstrate that 3D OCRT reveals 3D features unobserved by conventional OCT in fruit fly, zebrafish, and mouse samples.

Contactless optical coherence tomography of the eyes of freestanding individuals with a robotic scanner.

Clinical systems for optical coherence tomography (OCT) are used routinely to diagnose and monitor patients with a range of ocular diseases. They are large tabletop instruments operated by trained staff, and require mechanical stabilization of the head of the patient for positioning and motion reduction. Here we report the development and performance of a robot-mounted OCT scanner for the autonomous contactless imaging, at safe distances, of the eyes of freestanding individuals without the need for operator intervention or head stabilization. The scanner uses robotic positioning to align itself with the eye to be imaged, as well as optical active scanning to locate the pupil and to attenuate physiological eye motion. We show that the scanner enables the acquisition of OCT volumetric datasets, comparable in quality to those of clinical tabletop systems, that resolve key anatomic structures relevant for the management of common eye conditions. Robotic OCT scanners may enable the diagnosis and monitoring of patients with eye conditions in non-specialist clinics.

Application of multimodal MRI and radiologic features for stereotactic brain biopsy: insights from a series of 208 patients.

OBJECTIVES: We reviewed our institutional experience during a 10-year period for improvement of safety and efficacy of stereotactic biopsy procedures. METHODS: We performed a retrospective review of inpatient summaries, stereotactic worksheets and radiologic investigations of 208 consecutive patients, who underwent MRI-guided stereotactic biopsies between March 2010 and March 2020. RESULTS: The overall diagnostic yield was 96.2%. CT-confirmed intracranial hemorrhage occurred in 17 patients (8.2%), and the overall mortality rate was 0.5%. Combined MRS and PWI helped target selection in 27 cases (13.0%), the diagnostic yield was 100%. The results of the regression analysis revealed that non-diagnostic biopsy specimen significantly correlated with the cystic trait (p<.01) and edema of lesions (p<.05). Enhancement (p<.01) is shown to be an important factor for obtaining a diagnostic biopsy. Furthermore, the edema trait of lesions (p<.01) showed the important factors of hemorrhage. CONCLUSIONS: The radiological features of lesions and use of the most suitable MRI sequences during biopsy planning are recommended ways to improve the diagnostic yield and safety of this technique.

In vivo quantitative analysis of anterior chamber white blood cell mixture composition using spectroscopic optical coherence tomography.

Anterior uveitis is the most common form of intraocular inflammation, and one of its main signs is the presence of white blood cells (WBCs) in the anterior chamber (AC). Clinically, the true composition of cells can currently only be obtained using AC paracentesis, an invasive procedure to obtain AC fluid requiring needle insertion into the AC. We previously developed a spectroscopic optical coherence tomography (SOCT) analysis method to differentiate between populations of RBCs and subtypes of WBCs, including granulocytes, lymphocytes and monocytes, both in vitro and in ACs of excised porcine eyes. We have shown that different types of WBCs have distinct characteristic size distributions, extracted from the backscattered reflectance spectrum of individual cells using Mie theory. Here, we further develop our method to estimate the composition of blood cell mixtures, both in vitro and in vivo. To do so, we estimate the size distribution of unknown cell mixtures by fitting the distribution observed using SOCT with a weighted combination of reference size distributions of each WBC type calculated using kernel density estimation. We validate the accuracy of our estimation in an in vitro study, by comparing our results for a given WBC sample mixture with the cellular concentrations measured by a hemocytometer and SOCT images before mixing. We also conducted a small in vivo quantitative cell mixture validation pilot study which demonstrates congruence between our method and AC paracentesis in two patients with uveitis. The SOCT based method appears promising to provide quantitative diagnostic information of cellular responses in the ACs of patients with uveitis.