Fluid Segmentation Research Articles

Semi-supervised 3D retinal fluid segmentation via correlation mutual learning with global reasoning attention

Semi-supervised 3D retinal fluid segmentation via correlation mutual learning with global reasoning attention

Harmonizing Unets: Attention Fusion module in cascaded-Unets for low-quality OCT image fluid segmentation

Optical coherence tomography (OCT) is widely used for its high resolution. Accurate OCT image segmentation can significantly improve the diagnosis and treatment of retinal diseases such as Diabetic Macular Edema (DME). However, in resource-limited regions, portable devices with low-quality output are more frequently used, severely affecting the performance of segmentation. To address this issue, we propose a novel methodology in this paper, including a dedicated pre-processing pipeline and an end-to-end double U-shaped cascaded architecture, H-Unets. In addition, an Adaptive Attention Fusion (AAF) module is elaborately designed to improve the segmentation performance of H-Unets. To demonstrate the effectiveness of our method, we conduct a bunch of ablation and comparative studies on three open-source datasets. The experimental results show the validity of the pre-processing pipeline and H-Unets, achieving the highest Dice score of 90.60%±0.87% among popular methods in a relatively small model size.

Deep learning assessment of disproportionately enlarged subarachnoid-space hydrocephalus in Hakim’s disease or idiopathic normal pressure hydrocephalus

Abstract Background Disproportionately enlarged subarachnoid-space hydrocephalus (DESH) is a key feature of Hakim’s disease (synonymous with idiopathic normal pressure hydrocephalus; iNPH). However, it previously had been only subjectively evaluated. Purpose This study aims to evaluate the usefulness of MRI indices, derived from deep learning segmentation of cerebrospinal fluid (CSF) spaces, for DESH detection and to establish their optimal thresholds. Materials and Methods This study retrospectively enrolled a total of 1009 participants, including 77 patients diagnosed with Hakim’s disease, 380 healthy volunteers, 163 with mild cognitive impairment, 256 with Alzheimer’s disease, and 217 with other types of neurodegenerative diseases. DESH, ventriculomegaly, tightened sulci in the high convexities, and Sylvian fissure dilatation were evaluated on three-dimensional T1-weighted MRI by radiologists. The total ventricles, high-convexity part of the subarachnoid space, and Sylvian fissure and basal cistern were automatically segmented using the CSF Space Analysis application (FUJIFILM Corporation). Moreover, DESH, Venthi, and Sylhi indices were calculated based on these 3 regions. The area under the receiver-operating characteristic curves of these indices and region volumes (volume ratios) for DESH detection were calculated. Results Of the 1009 participants, 101 (10%) presented with DESH. The DESH, Venthi, and Sylhi indices performed well with 95.0%-96.0% sensitivity and 91.5%-96.8% specificity at optimal thresholds. All patients with Hakim’s disease were diagnosed with DESH, despite variations in severity. In patients with Hakim’s disease, with or without Alzheimer’s disease, the DESH index and total ventricular volume were significantly higher compared to patients with Alzheimer’s disease, although the total intracranial cerebrospinal fluid volume was significantly lower. Conclusion DESH, Venthi, and Sylhi indices, and the volumes and volume ratios of the ventricle and high-convexity part of the subarachnoid space computed using deep learning were useful for the DESH detection that may help to improve the diagnosis of Hakim’s disease (ie, iNPH).

Prediction of Post-Treatment Visual Acuity in Age-Related Macular Degeneration Patients With an Interpretable Machine Learning Method.

We evaluated the features predicting visual acuity (VA) after one year in neovascular age-related macular degeneration (nAMD) patients. A total of 527 eyes of 506 patients were included. Machine learning (ML) models were trained to predict VA deterioration beyond a logarithm of the minimum angle of resolution of 1.0 after 1 year based on the sequential addition of multimodal data. BaseM models used clinical data (age, sex, treatment regimen, and VA), SegM models included fluid volumes from optical coherence tomography (OCT) images, and RawM models used probabilities of visual deterioration (hereafter probability) from deep learning classifiers trained on baseline OCT (OCT0) and OCT after three loading doses (OCT3), fluorescein angiography, and indocyanine green angiography. We applied SHapley Additive exPlanations (SHAP) for machine learning model interpretation. The RawM model based on the probability of OCT0 outperformed the SegM model (area under the receiver operating characteristic curve of 0.95 vs. 0.91). Adding probabilities from OCT3, fluorescein angiography, and indocyanine green angiography to RawM showed minimal performance improvement, highlighting the practicality of using raw OCT0 data for predicting visual outcomes. Applied SHapley Additive exPlanations analysis identified VA after 3 months and OCT3 probability values as the most influential features over quantified fluid segments. Integrating multimodal data to create a visual predictive model yielded accurate, interpretable predictions. This approach allowed the identification of crucial factors for predicting VA in patients with nAMD. Interpreting a predictive model for 1-year VA in patients with nAMD from multimodal data allowed us to identify crucial factors for predicting VA.

Subject-specific atlas for automatic brain tissue segmentation of neonatal magnetic resonance images

Developing advanced systems for 3D brain tissue segmentation from neonatal magnetic resonance (MR) images is vital for newborn structural analysis. However, automatic segmentation of neonatal brain tissues is challenging due to smaller head size and inverted T1/T2 tissue contrast compared to adults. In this work, a subject-specific atlas based technique is presented for segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) from neonatal MR images. It involves atlas selection, subject-specific atlas creation using random forest (RF) classifier, and brain tissue segmentation using the expectation maximization-Markov random field (EM-MRF) method. To increase the segmentation accuracy, different tissue intensity- and gradient-based features were used. Evaluation on 40 neonatal MR images (gestational age of 37–44 weeks) demonstrated an overall accuracy of 94.3% and an average Dice similarity coefficient (DSC) of 0.945 (GM), 0.947 (WM), and 0.912 (CSF). Compared to multi-atlas segmentation methods like SEGMA and EM-MRF with multiple atlases, our method improved accuracy by up to 4%, particularly in complex tissue regions. Our proposed method allows accurate brain tissue segmentation, a crucial step in brain magnetic resonance imaging (MRI) applications including brain surface reconstruction and realistic head model creation in neonates.

GCN-Enhanced Spatial-Spectral Dual-Encoder Network for Simultaneous Segmentation of Retinal Layers and Fluid in OCT Images

Changes in retinal thickness and the morphology of fluid provide valuable diagnostic information for Macular Edema (ME). Optical Coherence Tomography (OCT) is a pivotal diagnostic tool for ME, and the accurate segmentation of OCT retinal images aids ophthalmologists in monitoring disease progression. However, the unpredictable nature of ME’s occurrence poses significant challenges for existing methods in achieving precise segmentation of both retinal layers and fluids simultaneously. In this study, a novel U-shaped encoder-decoder architecture named GD-Net, is introduced for simultaneously segmenting retinal layers of diverse thicknesses and fluids. Specifically, a novel Fast Fourier Encoder is integrated within the encoder branch to capture spectral domain features overlooked by the spatial encoder. Additionally, Multi-scale Graph Convolution module is inserted between the encoder and decoder branches to leverage retinal layer topology for spatial reasoning. GD-Net is further optimized using a joint loss function, which includes a weighted version of region segmentation loss and contour loss, effectively mitigating the issues of blurred fluid edges and internal holes. The method was validated on three publicly available datasets: DUKE DME, Peripapillary OCT, and RETOUCH, achieving overall mean Dice score of 0.839, 0.826, and 0.872, respectively, outperforming the other state-of-the-art techniques. GD-Net demonstrated consistent high-precision segmentation on B-scans from different devices, proving its robustness and surpassing other methods in terms of visual quality, thus offering substantial support for clinical ophthalmologists in diagnosing ME. The code and weights are publicly available at: https://github.com/DBook111/GD-Net.

Pretraining of 3D image segmentation models for retinal OCT using denoising-based self-supervised learning.

Deep learning algorithms have allowed the automation of segmentation for many biomarkers in retinal OCTs, enabling comprehensive clinical research and precise patient monitoring. These segmentation algorithms predominantly rely on supervised training and specialised segmentation networks, such as U-Nets. However, they require segmentation annotations, which are challenging to collect and require specialized expertise. In this paper, we explore leveraging 3D self-supervised learning based on image restoration techniques, that allow to pretrain 3D networks with the aim of improving segmentation performance. We test two methods, based on image restoration and denoising. After pretraining on a large 3D OCT dataset, we evaluate our weights by fine-tuning them on two challenging fluid segmentation datasets utilising different amount of training data. The chosen methods are easy to set up while providing large improvements for fluid segmentation, enabling the reduction of the amount of required annotation or an increase in the performance. Overall, the best results were obtained for denoising-based SSL methods, with higher results on both fluid segmentation datasets as well as faster pretraining durations.

Assessing CT-based Volumetric Analysis via Transfer Learning with MRI and Manual Labels for Idiopathic Normal Pressure Hydrocephalus.

Brain computed tomography (CT) is an accessible and commonly utilized technique for assessing brain structure. In cases of idiopathic normal pressure hydrocephalus (iNPH), the presence of ventriculomegaly is often neuroradiologically evaluated by visual rating and manually measuring each image. Previously, we have developed and tested a deep-learning-model that utilizes transfer learning from magnetic resonance imaging (MRI) for CT-based intracranial tissue segmentation. Accordingly, herein we aimed to enhance the segmentation of ventricular cerebrospinal fluid (VCSF) in brain CT scans and assess the performance of automated brain CT volumetrics in iNPH patient diagnostics. The development of the model used a two-stage approach. Initially, a 2D U-Net model was trained to predict VCSF segmentations from CT scans, using paired MR-VCSF labels from healthy controls. This model was subsequently refined by incorporating manually segmented lateral CT-VCSF labels from iNPH patients, building on the features learned from the initial U-Net model. The training dataset included 734 CT datasets from healthy controls paired with T1-weighted MRI scans from the Gothenburg H70 Birth Cohort Studies and 62 CT scans from iNPH patients at Uppsala University Hospital. To validate the model's performance across diverse patient populations, external clinical images including scans of 11 iNPH patients from the Universitatsmedizin Rostock, Germany, and 30 iNPH patients from the University of Alabama at Birmingham, United States were used. Further, we obtained three CT-based volumetric measures (CTVMs) related to iNPH. Our analyses demonstrated strong volumetric correlations (ϱ=0.91, p<0.001) between automatically and manually derived CT-VCSF measurements in iNPH patients. The CTVMs exhibited high accuracy in differentiating iNPH patients from controls in external clinical datasets with an AUC of 0.97 and in the Uppsala University Hospital datasets with an AUC of 0.99. CTVMs derived through deep learning, show potential for assessing and quantifying morphological features in hydrocephalus. Critically, these measures performed comparably to gold-standard neuroradiology assessments in distinguishing iNPH from healthy controls, even in the presence of intraventricular shunt catheters. Accordingly, such an approach may serve to improve the radiological evaluation of iNPH diagnosis/monitoring (i.e., treatment responses). Since CT is much more widely available than MRI, our results have considerable clinical impact.

Human versus Artificial Intelligence: Validation of a Deep Learning Model for Retinal Layer and Fluid Segmentation in Optical Coherence Tomography Images from Patients with Age-Related Macular Degeneration.

Artificial intelligence (AI) models have received considerable attention in recent years for their ability to identify optical coherence tomography (OCT) biomarkers with clinical diagnostic potential and predict disease progression. This study aims to externally validate a deep learning (DL) algorithm by comparing its segmentation of retinal layers and fluid with a gold-standard method for manually adjusting the automatic segmentation of the Heidelberg Spectralis HRA + OCT software Version 6.16.8.0. A total of sixty OCT images of healthy subjects and patients with intermediate and exudative age-related macular degeneration (AMD) were included. A quantitative analysis of the retinal thickness and fluid area was performed, and the discrepancy between these methods was investigated. The results showed a moderate-to-strong correlation between the metrics extracted by both software types, in all the groups, and an overall near-perfect area overlap was observed, except for in the inner segment ellipsoid (ISE) layer. The DL system detected a significant difference in the outer retinal thickness across disease stages and accurately identified fluid in exudative cases. In more diseased eyes, there was significantly more disagreement between these methods. This DL system appears to be a reliable method for accessing important OCT biomarkers in AMD. However, further accuracy testing should be conducted to confirm its validity in real-world settings to ultimately aid ophthalmologists in OCT imaging management and guide timely treatment approaches.

Improved Hybrid K-Nearest Neighbours Techniques in Segmentation of Low-Grade Tumor and Cerebrospinal Fluid

In image segmentation, identifying information or object detection in medical im-ages is crucial, particularly information that is harder to spot in magnetic resonance imaging (MRI) of low-grade tumors or cerebrospinal fluid (CSF). To address the aforementioned problems associated with missing data in MRI images and the low quality of MRI images that required longer processing times, this research is to seg-ment brain tumors or detect CSF in four-dimensional MRI images. A new hybrid k-nearest neighbors (k-NN) framework is also proposed, which consists of three tech-niques: correlation matrices of discrete Fourier transform (CM-DFT), Laplace Eigen maps of locally preserving projection (LELPP), and a hybrid GrabCut hidden Markov model of k-mean clustering (GCHMkC). The combination of the Hidden Markov Model (HMM) and the k-mean clustering technique is known as the Hidden Markov Model of the k-mean clustering method (HMMkC). To begin with, the Graph Cut and Support Vector Machine (GCSVM) and the GCHMkC approach are combined. The method increased the quality of the images suggested by the methodology, achieving an accuracy of 99.83%, a sensitivity of 99.99%, a specificity of 99.8%, and a computa-tional execution time of 14.9 seconds. Second, a technique called CM-DFT is sug-gested to improve MRI images while resolving the issue of missing imputation data. The accuracy of the MRI image datasets was improved to 99.84%, the time-lag in the hybrid k-NN algorithm was reduced to 99%, the missing data ratio was reduced to 0.9%, 10%, and 12%, and the correctness of the imputed data was improved to 1.533 seconds with computational execution. Thirdly, the nonlinear data is reduced and unnecessary features are eliminated using the Laplace Eigen maps of locally pre-serving projection (LELPP) approach. The hybrid k-NN algorithm used by the tech-nique yields results with 99% accuracy and an execution duration of 2.42 seconds.

Reservoir fluid identification based on multi-head attention with UMAP

Gas logging curves obtained during drilling, along with conventional well logs, typically serve as foundational data for identifying reservoir fluids. Traditional methods for identifying layers containing oil or gas often suffer from limitations such as inadequate feature extraction, underutilization of differences in data points, and limited applicability. These limitations lead to reduced accuracy in predicting reservoir fluid segmentation. To address the challenge of spatiotemporal feature extraction in logging curves, this paper presents a fluid identification method based on deep learning networks and multivariate sequence classification. The method takes into account both the sequential variations in geological strata and the relationships among different curves for fluid prediction. Our proposed method utilizes a Transformer multi-head attention mechanism and follows a series of steps. First, an improved Symbolic Aggregate approXimation (SAX) is employed to assess the response characteristics of the logging curves to the target fluid and identify sensitive curves based on information similarity. Subsequently, the semi-supervised learning method UMAP is applied to identify layers with high interpretability. Hierarchical training is then employed to mitigate the impact of noise and high-dimensional nonlinearity on identification results. Finally, the processed vector curves are input into the attention module layer, where features of each layer are extracted in an encoding-decoding manner. This method has been tested and validated using gas logging data from 12 wells in a gas field offshore China. The overall prediction accuracy can exceed 94%, and the prediction accuracy for a single gas layer is over 88%. When compared to other machine learning approaches, such as K-means, decision tree, and XGBoost, this method exhibits superior identification accuracy and generalization ability, especially for unbalanced samples.

FNeXter: A Multi-Scale Feature Fusion Network Based on ConvNeXt and Transformer for Retinal OCT Fluid Segmentation.

The accurate segmentation and quantification of retinal fluid in Optical Coherence Tomography (OCT) images are crucial for the diagnosis and treatment of ophthalmic diseases such as age-related macular degeneration. However, the accurate segmentation of retinal fluid is challenging due to significant variations in the size, position, and shape of fluid, as well as their complex, curved boundaries. To address these challenges, we propose a novel multi-scale feature fusion attention network (FNeXter), based on ConvNeXt and Transformer, for OCT fluid segmentation. In FNeXter, we introduce a novel global multi-scale hybrid encoder module that integrates ConvNeXt, Transformer, and region-aware spatial attention. This module can capture long-range dependencies and non-local similarities while also focusing on local features. Moreover, this module possesses the spatial region-aware capabilities, enabling it to adaptively focus on the lesions regions. Additionally, we propose a novel self-adaptive multi-scale feature fusion attention module to enhance the skip connections between the encoder and the decoder. The inclusion of this module elevates the model's capacity to learn global features and multi-scale contextual information effectively. Finally, we conduct comprehensive experiments to evaluate the performance of the proposed FNeXter. Experimental results demonstrate that our proposed approach outperforms other state-of-the-art methods in the task of fluid segmentation.

Enhancing point annotations with superpixel and confident learning guided for improving semi-supervised OCT fluid segmentation

Automatic segmentation of fluid in Optical Coherence Tomography (OCT) images is beneficial for ophthalmologists to make an accurate diagnosis. Although semi-supervised OCT fluid segmentation networks enhance their performance by introducing additional unlabeled data, the performance enhancement is limited. To address this, we propose Superpixel and Confident Learning Guide Point Annotations Network (SCLGPA-Net) based on the teacher-student architecture, which can learn OCT fluid segmentation from limited fully-annotated data and abundant point-annotated data. Specifically, we use points to annotate fluid regions in unlabeled OCT images and the Superpixel-Guided Pseudo-Label Generation (SGPLG) module generates pseudo-labels and pixel-level label trust maps from the point annotations. The label trust maps provide an indication of the reliability of the pseudo-labels. Furthermore, we propose the Confident Learning Guided Label Refinement (CLGLR) module identifies error information in the pseudo-labels and leads to further refinement. Experiments on the RETOUCH dataset show that we are able to reduce the need for fully-annotated data by 94.22%, closing the gap with the best fully supervised baselines to a mean IoU of only 2%. Furthermore, we constructed a private 2D OCT fluid segmentation dataset for evaluation. Compared with other methods, comprehensive experimental results demonstrate that the proposed method can achieve excellent performance in OCT fluid segmentation.

AMSC-Net: Anatomy and multi-label semantic consistency network for semi-supervised fluid segmentation in retinal OCT

Automated segmentation of pathological fluid regions is crucial for digital diagnosis and individualized therapy under optical coherence tomography (OCT) images. Nonetheless, methods that rely on enormous annotations impede their clinical applications, as pixel-wise fine-grained labels are extraordinarily costly and require the expertise of ophthalmology. Though there exist works based on consistency-based and pseudo-label-based methods for reducing annotation reliance, the underutilization of consistency mechanisms and ignorance of fluid anatomical structures result in sub-optimal segmentation performance. In this work, we proposed a novel AMSC-Net devoted to semi-supervised fluid segmentation and achieves a 73.95% Dice score with 5% labeled data. Concretely, we developed a Heterogeneous Architecture Consistency (HAC) strategy based on our dual decoders enjoying different inductive biases. Moreover, we invented a Multi-label Semantic Consistency Loss (MSC-Loss) module in hierarchical semantic features and an Anatomy Contour Consistency Loss (ACC-Loss) module integrating anatomical constraint. These modules complementarily reinforce the quality of pseudo labels and boost semi-supervised training for robust segmentation results. To verify the superiority and bedside potential of our proposed AMSC-Net, we collected a large-scale fluid segmentation dataset composed of 22517 OCT images. Extensive quantitative and qualitative experiments validated the efficacy of our AMSC-Net with multiple novel techniques. Also, experimental results in a public fluid segmentation dataset demonstrated that our method achieves state-of-the-art performance. Code will be available at: https://github.com/ZeroOneGame/S4_Fluid_OCT.

SCHWARTZ-MATSUO SYNDROME : AN IMPORTANT CAUSE OF SECONDARY GLAUCOMA : RARE CASE

Introduction : Schwartz-Matsuo syndrome is a rare ocular condition characterized by rhegmatogenous retinal detachment, elevated intraocular pressure, and open anterior chamber angle with presence of aqueous cells. The prevalence of Schwartz-Matsuo syndrome is quite low and only reported through case studies.&#x0D; Case Illustration : A 63 years old female complained a visual aquity was no light perception on the left eye (LE) since 2 months ago. The symptoms are gradually getting worse, history of ocular trauma, recent surgery, photopsia, peripheral visual field loss were denied. Patient came to the hospital with IOP elevation on left eye. On Gonioscopy we interpretated as open angle chamber and USG examination we founded retinal detachment, Anti-glaucoma medication were given but no success to lower the IOP.&#x0D; Discussion : Typically, retinal detachment is associated with low IOP. In Schwartz-Matsuo syndrome the opposite is observed with elevated IOP. Hypothesized that photoreceptor outer segments pass through the retinal break allows for fluid and photoreceptor outer segments to gain access, enter the anterior chamber and block aqueous outflow at the level of the trabecular meshwork. A significant increase in IOP can lead to visual loss and blindness complicated by glaucoma requiring further medical or surgical intervention. Referred to this patient, she came with IOP elevation, open angle anterior chamber and retinal detachment that clinically diagnosed as Schwartz-Matsuo syndrome.&#x0D; Conclusion : Schwartz-Matsuo syndrome is primarily a clinical diagnosis; Early detection of disease and prompt management of increased IOP and retinal detachment is essential for a good outcome.

Performance Evaluation of Retinal OCT Fluid Segmentation, Detection, and Generalization Over Variations of Data Sources

Performance Evaluation of Retinal OCT Fluid Segmentation, Detection, and Generalization Over Variations of Data Sources

Synthetic MRI Generation from CT Scans for Stroke Patients

CT scans are currently the most common imaging modality used for suspected stroke patients due to their short acquisition time and wide availability. However, MRI offers superior tissue contrast and image quality. In this study, eight deep learning models are developed, trained, and tested using a dataset of 181 CT/MR pairs from stroke patients. The resultant synthetic MRIs generated by these models are compared through a variety of qualitative and quantitative methods. The synthetic MRIs generated by a 3D UNet model consistently demonstrated superior performance across all methods of evaluation. Overall, the generation of synthetic MRIs from CT scans using the methods described in this paper produces realistic MRIs that can guide the registration of CT scans to MRI atlases. The synthetic MRIs enable the segmentation of white matter, grey matter, and cerebrospinal fluid by using algorithms designed for MRIs, exhibiting a high degree of similarity to true MRIs.

Loss-balanced parallel decoding network for retinal fluid segmentation in OCT

As a leading cause of blindness worldwide, macular edema (ME) is mainly determined by sub-retinal fluid (SRF), intraretinal fluid (IRF), and pigment epithelial detachment (PED) accumulation, and therefore, the characterization of SRF, IRF, and PED, which is also known as ME segmentation, has become a crucial issue in ophthalmology. Due to the subjective and time-consuming nature of ME segmentation in retinal optical coherence tomography (OCT) images, automatic computer-aided systems are highly desired in clinical practice. This paper proposes a novel loss-balanced parallel decoding network, namely PadNet, for ME segmentation. Specifically, PadNet mainly consists of an encoder and three parallel decoder modules, which serve as segmentation, contour, and diffusion branches, and they are employed to extract the ME’s characteristics, the contour area features, and to expand the ME area from the center to edge, respectively. A new loss-balanced joint-loss function with three components corresponding to each of the three parallel decoding branches is also devised for training. Experiments are conducted with three public datasets to verify the effectiveness of PadNet, and the performances of PadNet are compared with those of five state-of-the-art methods. Results show that PadNet improves ME segmentation accuracy by 8.1%, 11.1%, 0.6%, 1.4% and 8.3%, as compared with UNet, sASPP, MsTGANet, YNet, RetiFluidNet, respectively, which convincingly demonstrates that the proposed PadNet is robust and effective in ME segmentation in different cases.

Effects of atlas-based anatomy on modelled light transport in the neonatal head

Objective. Diffuse optical tomography (DOT) provides a relatively convenient method for imaging haemodynamic changes related to neuronal activity on the cerebral cortex. Due to practical challenges in obtaining anatomical images of neonates, an anatomical framework is often created from an age-appropriate atlas model, which is individualized to the subject based on measurements of the head geometry. This work studies the approximation error arising from using an atlas instead of the neonate's own anatomical model. Approach. We consider numerical simulations of frequency-domain (FD) DOT using two approaches, Monte Carlo simulations and diffusion approximation via finite element method, and observe the variation in (1) the logarithm of amplitude and phase shift measurements, and (2) the corresponding inner head sensitivities (Jacobians), due to varying segmented anatomy. Varying segmentations are sampled by registering 165 atlas models from a neonatal database to the head geometry of one individual selected as the reference model. Prior to the registration, we refine the segmentation of the cerebrospinal fluid (CSF) by separating the CSF into two physiologically plausible layers. Main results. In absolute measurements, a considerable change in the grey matter or extracerebral tissue absorption coefficient was found detectable over the anatomical variation. In difference measurements, a small local 10%-increase in brain absorption was clearly detectable in the simulated measurements over the approximation error in the Jacobians, despite the wide range of brain maturation among the registered models. Significance. Individual-level atlas models could potentially be selected within several weeks in gestational age in DOT difference imaging, if an exactly age-appropriate atlas is not available. The approximation error method could potentially be implemented to improve the accuracy of atlas-based imaging. The presented CSF segmentation algorithm could be useful also in other model-based imaging modalities. The computation of FD Jacobians is now available in the widely-used Monte Carlo eXtreme software.

AmnioML: Amniotic Fluid Segmentation and Volume Prediction with Uncertainty Quantification

AmnioML: Amniotic Fluid Segmentation and Volume Prediction with Uncertainty Quantification