Semi-automatic Segmentation Method Research Articles

Immunoexpression of IL-33 in the different clinical aspects of canine atopic dermatitis

Canine atopic dermatitis (CAD) is a chronic and inflammatory skin condition with a multifaceted origin, involving genetic factors, skin barrier abnormalities, immune responses, and hypersensitivity to various allergens. Interleukin 33 (IL-33), released by keratinocytes upon cellular injury, plays a crucial role in atopic dermatitis pathogenesis by inducing Th2 lymphocyte-mediated immune responses. This study aimed to evaluate IL-33 expression in dogs with atopic dermatitis and compare it to a control group. Forty-nine dogs were included, with 39 having atopic dermatitis, subdivided into groups based on clinical characteristics, and ten in the control group. Lesion and pruritus scores were assessed, and incisional biopsies were analyzed for dermatopathological characteristics. IL-33 expression was evaluated using immunohistochemistry, the analyses were blinded, based on the measurement of immunostaining areas using Image Pro-Plus software, version 4.5, relying on a semi-automatic color segmentation method, where the tissue immunostaining area for each biomarker was artificially delimited and quantified. Statistically significant differences in IL-33 immunostaining were found among groups (P=0.0005). Lichenified dogs (group 4) exhibited higher immunostaining compared to erythema (group 3) (P=0.0006), alesional pruritus (group 2) (P=0.0261), and the control group (group 1) (P=0.0079). IL-33 immunostaining increased with lesion progression, strongly correlating with lesion scores (P<0.0001), particularly in patients with chronic lesions characterized by erythema and lichenification. These findings suggest IL-33's significant role in canine atopic dermatitis pathogenesis and its association with lesion and inflammation scores during the chronic phase. This suggests potential therapeutic interventions targeting IL-33 or its receptors, though further studies are needed to explore these possibilities.

An open-source, three-dimensional growth model of the mandible

The available reference data for the mandible and mandibular growth consists primarily of two-dimensional linear or angular measurements. The aim of this study was to create the first open-source, three-dimensional statistical shape model of the mandible that spans the complete growth period. Computed tomography scans of 678 mandibles from children and young adults between 0 and 22 years old were included in the model. The mandibles were segmented using a semi-automatic or automatic (artificial intelligence-based) segmentation method. Point correspondence among the samples was achieved by rigid registration, followed by non-rigid registration of a symmetrical template onto each sample. The registration process was validated with adequate results. Principal component analysis was used to gain insight in the variation within the dataset and to investigate age-related changes and sexual dimorphism. The presented growth model is accessible globally and free-of-charge for scientists, physicians and forensic investigators for any kind of purpose deemed suitable. The versatility of the model opens up new possibilities in the fields of oral and maxillofacial surgery, forensic sciences or biological anthropology. In clinical settings, the model may aid diagnostic decision-making, treatment planning and treatment evaluation.

Comparison of semi-automatic and manual segmentation methods for tumor delineation on head and neck squamous cell carcinoma (HNSCC) positron emission tomography (PET) images

Objective. Accurate and reproducible tumor delineation on positron emission tomography (PET) images is required to validate predictive and prognostic models based on PET radiomic features. Manual segmentation of tumors is time-consuming whereas semi-automatic methods are easily implementable and inexpensive. This study assessed the reliability of semi-automatic segmentation methods over manual segmentation for tumor delineation in head and neck squamous cell carcinoma (HNSCC) PET images. Approach. We employed manual and six semi-automatic segmentation methods (just enough interaction (JEI), watershed, grow from seeds (GfS), flood filling (FF), 30% SUVmax and 40%SUVmax threshold) using 3D slicer software to extract 128 radiomic features from FDG-PET images of 100 HNSCC patients independently by three operators. We assessed the distributional properties of all features and considered 92 log-transformed features for subsequent analysis. For each paired comparison of a feature, we fitted a separate linear mixed effect model using the method (two levels; manual versus one semi-automatic method) as a fixed effect and the subject and the operator as the random effects. We estimated different statistics—the intraclass correlation coefficient agreement (aICC), limits of agreement (LoA), total deviation index (TDI), coverage probability (CP) and coefficient of individual agreement (CIA)—to evaluate the agreement between the manual and semi-automatic methods. Main results. Accounting for all statistics across 92 features, the JEI method consistently demonstrated acceptable agreement with the manual method, with median values of aICC = 0.86, TDI = 0.94, CP = 0.66, and CIA = 0.91. Significance. This study demonstrated that JEI method is a reliable semi-automatic method for tumor delineation on HNSCC PET images.

A Growth Prediction Model of Pulmonary Ground-Glass Nodules Based on Clinical Visualization Parameters

Objective To establish a model for predicting the growth of pulmonary ground-glass nodules (GGN) based on the clinical visualization parameters extracted by the 3D reconstruction technique and to verify the prediction performance of the model. Methods A retrospective analysis was carried out for 354 cases of pulmonary GGN followed up regularly in the outpatient of pulmonary nodules in Zhoushan Hospital of Zhejiang Province from March 2015 to December 2022.The semi-automatic segmentation method of 3D Slicer was employed to extract the quantitative imaging features of nodules.According to the follow-up results,the nodules were classified into a resting group and a growing group.Furthermore,the nodules were classified into a training set and a test set by the simple random method at a ratio of 7∶3.Clinical and imaging parameters were used to establish a prediction model,and the prediction performance of the model was tested on the validation set. Results A total of 119 males and 235 females were included,with a median age of 55.0 (47.0,63.0) years and the mean follow-up of (48.4±16.3) months.There were 247 cases in the training set and 107 cases in the test set.The binary Logistic regression analysis showed that age (95%CI=1.010-1.092,P=0.015) and mass (95%CI=1.002-1.067,P=0.035) were independent predictors of nodular growth.The mass (M) of nodules was calculated according to the formula M=V×(CTmean+1000)×0.001 (where V is the volume,V=3/4πR3,R:radius).Therefore,the logit prediction model was established as ln[P/(1-P)]=-1.300+0.043×age+0.257×two-dimensional diameter+0.007×CTmean.The Hosmer-Lemeshow goodness of fit test was performed to test the fitting degree of the model for the measured data in the validation set (χ2=4.515,P=0.808).The check plot was established for the prediction model,which showed the area under receiver-operating characteristic curve being 0.702. Conclusions The results of this study indicate that patient age and nodule mass are independent risk factors for promoting the growth of pulmonary GGN.A model for predicting the growth possibility of GGN is established and evaluated,which provides a basis for the formulation of GGN management strategies.

Development and validation of a semi-automated and unsupervised method for femur segmentation from CT

Quantitative computed tomography (QCT)-based in silico models have demonstrated improved accuracy in predicting hip fractures with respect to the current gold standard, the areal bone mineral density. These models require that the femur bone is segmented as a first step. This task can be challenging, and in fact, it is often almost fully manual, which is time-consuming, operator-dependent, and hard to reproduce. This work proposes a semi-automated procedure for femur bone segmentation from CT images. The proposed procedure is based on the bone and joint enhancement filter and graph-cut algorithms. The semi-automated procedure performances were assessed on 10 subjects through comparison with the standard manual segmentation. Metrics based on the femur geometries and the risk of fracture assessed in silico resulting from the two segmentation procedures were considered. The average Hausdorff distance (0.03 ± 0.01 mm) and the difference union ratio (0.06 ± 0.02) metrics computed between the manual and semi-automated segmentations were significantly higher than those computed within the manual segmentations (0.01 ± 0.01 mm and 0.03 ± 0.02). Besides, a blind qualitative evaluation revealed that the semi-automated procedure was significantly superior (p < 0.001) to the manual one in terms of fidelity to the CT. As for the hip fracture risk assessed in silico starting from both segmentations, no significant difference emerged between the two (R2 = 0.99). The proposed semi-automated segmentation procedure overcomes the manual one, shortening the segmentation time and providing a better segmentation. The method could be employed within CT-based in silico methodologies and to segment large volumes of images to train and test fully automated and supervised segmentation methods.

Automatic and semi-automatic segmentation method of post-myocardial infarction according to magnetic resonance imaging with late gadolinium enhancement

Automatic and semi-automatic segmentation method of post-myocardial infarction according to magnetic resonance imaging with late gadolinium enhancement

Computed tomography-based body composition parameters can predict short-term prognosis in ulcerative colitis patients.

Emerging evidence suggests a potential relationship between body composition and short-term prognosis of ulcerative colitis (UC). Early and accurate assessment of rapid remission based on conventional therapy via abdominal computed tomography (CT) images has rarely been investigated. This study aimed to build a prediction model using CT-based body composition parameters for UC risk stratification. In total, 138 patients with abdominal CT images were enrolled. Eleven quantitative parameters related to body composition involving skeletal muscle mass, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) were measured and calculated using a semi-automated segmentation method. A prediction model was established with significant parameters using a multivariable logistic regression. The receiver operating characteristic (ROC) curve was plotted to evaluate prediction performance. Subgroup analyses were implemented to evaluate the diagnostic efficiency of the prediction model between different disease locations, centers, and CT scanners. The Delong test was used for statistical comparison of ROC curves. VAT density, SAT density, gender, and visceral obesity were significantly statistically different between remission and invalidation groups (all p < 0.05). The accuracy, sensitivity, specificity, and area under the ROC curve (AUC) of the prediction model were 82.61%, 95.45%, 69.89%, and 0.855 (0.792-0.917), respectively. The positive predictive value and negative predictive value were 70.79% and 93.88%, respectively. No significant differences in the AUC of the prediction model were found in different subgroups (all p > 0.05). The predicting model constructed with CT-based body composition parameters is a potential non-invasive approach for short-term prognosis identification and risk stratification. Additionally, VAT density was an independent predictor for escalating therapeutic regimens in UC cohorts. The CT images were used for evaluating body composition and risk stratification of ulcerative colitis patients, and a potential non-invasive prediction model was constructed to identify non-responders with conventional therapy for making therapeutic regimens timely and accurately. • CT-based prediction models help divide patients into invalidation and remission groups in UC. • Results of the subgroup analysis confirmed the stability of the prediction model with a high AUC (all > 0.820). • The visceral adipose tissue density was an independent predictor of bad short-term prognosis in UC.

20. IMPLEMENTATION OF A SEMI-AUTOMATED SEGMENTATION TOOL FOR COLON SURGICAL PLANNING

Abstract Introduction This study outlines the design and implementation of an application focused on optimizing surgical planning for colon surgery through the automated segmentation process of Computed Axial Tomography (CT) images. The tool provides continuous assistance to the user, enhancing the overall process management and efficiency. Methods A module has been developed for the 3DSlicer image computing platform. The algorithm was programmed in Python, primarily utilizing the Slicer, MRML, and VTK libraries. Additionally, DICOM images obtained from a publicly available database were used as samples for the study. Results The implementation of the semi-automatic segmentation method has proved to be successful, achieving the creation of a three-dimensional (3D) model of the colon with satisfactory results. The user has the facility to upload a CT image from a DICOM study, and through few interactions, a 3D model of the colon is generated, optimizing the surgical planning process. Conclusion Although we have successfully developed a semi-automatic segmentation method, the process requires certain specific conditions. For the image to be interpreted correctly, the patient must be treated with iodinated contrast medium prior to the CT study. In addition, an insufflation of the colon should be performed. These prerequisites are essential to ensure the quality and accuracy of the resulting images, which in turn will impact the efficiency of the planning process.

Deep learning-based segmentation of breast masses using convolutional neural networks

Automatic breast tumor segmentation based on convolutional neural networks (CNNs) is significant for the diagnosis and monitoring of breast cancers. CNNs have become an important method for early diagnosis of breast cancer and, thus, can help decrease the mortality rate. In order to assist medical professionals in breast cancer investigation a computerized system based on two encoder-decoder architectures for breast tumor segmentation has been developed. Two pre-trained DeepLabV3+ and U-Net models are proposed. The encoder generates a high-dimensional feature vector while the decoder analyses the low-resolution feature vector provided by the encoder and generates a semantic segmentation mask. Semantic segmentation based on deep learning techniques can overcome the limitations of traditional algorithms. To assess the efficiency of breast ultrasound image segmentation, we compare the segmentation results provided by CNNs against the Local Graph Cut technique (a semi-automatic segmentation method) in the Image Segmenter application. The output segmentation results have been evaluated by using the Dice similarity coefficient that compares the ground truth images provided by the specialists against the predicted segmentation results provided by the CNNs and Local Graph Cut algorithm. The proposed approach is validated on 780 breast ultrasonographic images of the BUSI public database of which 437 are benign and 210 are malignant. The BUSI database provides classification (benign or malignant) labels for ground truth in binary mask images. The average Dice scores computed between the ground truth images against CNNs were as follows: 0.9360 (malignant) and 0.9325 (benign) for the DeepLabV3+ architecture and of 0.6251 (malignant) and 0.6252 (benign) for the U-Net, respectively. When the segmentation results provided by CNNs were compared with the Local Graph Cut segmented images, the Dice scores were 0.9377 (malignant) and 0.9204 (benign) for DeepLabV3+ architecture and 0.6115 (malignant) and 0.6119 (benign) for U-Net, respectively. The results show that the DeepLabV3+ has significantly better segmentation performance and outperforms the U-Net network.

Robust semi-automatic segmentation method: an expert assistant tool for muscles in CT and MR data

ABSTRACT Image muscle segmentation is useful to quantitatively assess musculoskeletal diseases by extracting biomarkers such as shape, texture and water diffusivity metrics. Although volumetric manual segmentation is time consuming and a bottleneck in practice, fully automatic approaches are still in progress to reach an acceptable accuracy. In this paper, we provide a robust semi-automated tool to segment two musculoskeletal systems, i.e. thigh and shoulder in MRI and CT modalities, respectively. The tool only needs a few manually labelled cross-sections to build a directed graph-structure of corresponding points between the successive spaced slices. The boundaries of each muscle are obtained by performing a spline interpolation based on the directed graph-structure. Each muscle label and its corresponding 3D mesh are deduced using post-processing techniques. We evaluated the tool on 26 MRI thighs and 16 CT shoulders. Three metrics along with inter-muscle overlapping were employed to evaluate the tool by comparison to an expert manual segmentation and a publicly available tools (ITK-SNAP, 3D Slicer). The results showed a mean Dice 0.988 ± 0.003 , and Hausdorff Distance 4.86 ± 1.67 mm in comparison to the manual reference for thigh muscle segmentation, and a mean Dice 0.961 ± 0.005 and Hausdorff Distance 2.42 ± 0.79 mm for shoulder muscle segmentation, outperformed the other methods. The tool is proposed as slicer module available at https://github.com/latimagine/SlicerSpline.

GPU-accelerated lung CT segmentation based on level sets and texture analysis

This paper presents a novel semi-automatic method for lung segmentation in thoracic CT datasets. The fully three-dimensional algorithm is based on a level set representation of an active surface and integrates texture features to improve its robustness. The method’s performance is enhanced by the graphics processing unit (GPU) acceleration. The segmentation process starts with a manual initialisation of 2D contours on a few representative slices of the analysed volume. Next, the starting regions for the active surface are generated according to the probability maps of texture features. The active surface is then evolved to give the final segmentation result. The recent implementation employs features based on grey-level co-occurrence matrices and Gabor filters. The algorithm was evaluated on real medical imaging data from the LCTCS 2017 challenge. The results were also compared with the outcomes of other segmentation methods. The proposed approach provided high segmentation accuracy while offering very competitive performance.

Comparative evaluation of the accuracy of 3D TMJ analysis performed by different methods of processing computed tomograms

The aim of this study. Comparison of the accuracy of segmentation of TMJ elements in different ways and assessment of the suitability of the data obtained for the diagnosis of TMJ dysfunction. To study the segmentation of the bone elements of the TMJ (articular fossa, head of the LF), 60 computed tomograms of the maxillofacial region of patients were randomly selected in various ways (archival material). In group 1, the results of CT processing by AI diagnostics algorithms (Russia) were collected; in group 2, the results of CT processing based on the semi-automatic segmentation method in the Avantis3D program. The results of CT processing by Avantis3D AI algorithms (Russia) with different probability modes - 0.4 and 0.9, respectively, were selected for the third and fourth groups. Visually, the coincidence of the contours of the LF heads and articular pits isolated using different methods with their contours on all possible sections of the original CT itself was evaluated. The time spent on TMJ segmentation according to CT data was determined and compared using the methods described above. Of the 240 objects, only 7.5% of the cases showed a slight discrepancy between the contours of the original CT in group b1, which was the lowest of all. A slight discrepancy in the TMJ contours to be corrected is characteristic of the semi-automatic method of segmentation by optical density was detected in 50.4% (group 2). The largest percentage of significant errors not subject to correction was noted in the first group, which made it impossible to perform a full 3D analysis of the TMJ, and the smallest in the second and fourth. The magnitude of the error in determining the width of the articular gap in different groups is comparable to the size of one voxel per CT. When segmentation is carried out using AI, the difference between segmented objects is close to zero values. The average time spent on TMJ segmentation in group 1 was 10.2±1.23 seconds, in group 2 - 12.6±1.87 seconds, in groups 3 and 4 - 0.46±0.12 seconds and 0.46±0.13 seconds, respectively. The developed automated method for segmenting TMJ elements using AI is obviously more suitable for practical work, since it requires minimal time, and is almost as accurate as other methods under consideration.

A semi-automatic segmentation method for meningioma developed using a variational approach model.

Meningioma is the commonest primary brain tumour. Volumetric post-contrast magnetic resonance imaging (MRI) is recognised as gold standard for delineation of meningioma volume but is hindered by manual processing times. We aimed to investigate the utility of a model-based variational approach in segmenting meningioma. A database of patients with a meningioma (2007-2015) was queried for patients with a contrast-enhanced volumetric MRI, who had consented to a research tissue biobank. Manual segmentation by a neuroradiologist was performed and results were compared to the mathematical model, using a battery of tests including the Sørensen-Dice coefficient (DICE) and JACCARD index. A publicly available meningioma dataset (708 segmented T1 contrast-enhanced slices) was also used to test the reliability of the model. 49 meningioma cases were included. The most common meningioma location was convexity (n = 15, 30.6%). The mathematical model segmented all but one incidental meningioma, which failed due to the lack of contrast uptake. The median meningioma volume by manual segmentation was 19.0cm3 (IQR 4.9-31.2). The median meningioma volume using the mathematical model was 16.9cm3 (IQR 4.6-28.34). The mean DICE score was 0.90 (SD = 0.04). The mean JACCARD index was 0.82 (SD = 0.07). For the publicly available dataset, the mean DICE and JACCARD scores were 0.90 (SD = 0.06) and 0.82 (SD = 0.10), respectively. Segmentation of meningioma volume using the proposed mathematical model was possible with accurate results. Application of this model on contrast-enhanced volumetric imaging may help reduce work burden on neuroradiologists with the increasing number in meningioma diagnoses.

Local graph cut in the Image Segmenter app for breast ultrasound images segmentation

Breast cancer is among the most common cancers diagnosed in women globally. To help the breast cancer diagnosis, an important step is to accurately segment the breast lesion. To support clinicians in this important step, we analyze the performance of a semi-automated segmentation method based on the Local Graph Cut technique in the Image Segmenter application. Local graph cuts algorithm has the ability to segment more complicated shape by converting the image into a graph representation. It employs seed points set by the user and a cost function. The user identifies certain pixels as foreground and background. The region properties are identified from these pixels and they allow to specify the probability of a pixel belonging to the background or foreground. The graph cut formulation assigns each pixel to a node in the graph and incorrectly segmented pixels are re-assigned until the desired segmentation is completed. To evaluate the segmentation results, the Dice similarity coefficient and Fréchet distance were calculated between the ground truth images and the segmented images. Results show a Dice score of 0.7754 for malignant lesions and 0.8842 for benign lesions. The average Fréchet distance values were 303.28 for malignant and 290.80 for benign lesions, respectively. The experimental results show that the method achieves the best performance and gets the higher Dice score and Fréchet distance for breast benign lesions against malignant lesions.

Calculation of Kidney Volumes with Magnetic Resonance in Patients with Autosomal Dominant Polycystic Kidney Disease: Comparison between Methods

Autosomal dominant polycystic renal disease (ADPKD) is the most frequent kidney inheritable disease, characterized by the presence of numerous bilateral renal cysts, causing a progressive increase in total kidney volume (TKV) and a progressive loss of renal function. Several methods can be used to measure TKV by using MRI, and they differ in complexity, accuracy and time consumption. This study was performed to assess the performance of the ellipsoid method and the semi-automatic segmentation method, both for TKV and SKV (single kidney volume) computation. In total, 40 patients were enrolled, and 78 polycystic kidneys analyzed. Two independent operators with different levels of experience evaluated renal volumetry using both methods. Mean error for ellipsoid method for SKV computation was −2.74 ± 11.79% and 3.25 ± 10.02% for the expert and the beginner operator, respectively (p = 0.0008). A Wilcoxon test showed a statistically significant difference between the two operators for both methods (SKV p = 0.0371 and 0.0034; TKV p = 0.0416 and 0.0171 for the expert and the beginner operator, respectively). No inter-operator significant difference was found for the semi-automatic method, in contrast to the ellipsoid method. Both with a Wilcoxon test and Bland–Altman plot, statistically significant differences were found when comparing SKV and TKV measurements obtained with the two methods for both operators, even if the differences are stronger for the beginner operator than for the expert one. The semi-automatic segmentation method showed more inter-observer reproducibility. The ellipsoid method, in contrast, appears to be affected by greater inter-observer variability, especially when performed by operators with limited experience.

Radiomics Prediction of Muscle Invasion in Bladder Cancer Using Semi-Automatic Lesion Segmentation of MRI Compared with Manual Segmentation.

Conventional radiomics analysis requires the manual segmentation of lesions, which is time-consuming and subjective. This study aimed to assess the feasibility of predicting muscle invasion in bladder cancer (BCa) with radiomics using a semi-automatic lesion segmentation method on T2-weighted images. Cases of non-muscle-invasive BCa (NMIBC) and muscle-invasive BCa (MIBC) were pathologically identified in a training cohort and in internal and external validation cohorts. For bladder tumor segmentation, a deep learning-based semi-automatic model was constructed, while manual segmentation was performed by a radiologist. Semi-automatic and manual segmentation results were respectively used in radiomics analyses to distinguish NMIBC from MIBC. An equivalence test was used to compare the models' performance. The mean Dice similarity coefficients of the semi-automatic segmentation method were 0.836 and 0.801 in the internal and external validation cohorts, respectively. The area under the receiver operating characteristic curve (AUC) were 1.00 (0.991) and 0.892 (0.894) for the semi-automated model (manual) on the internal and external validation cohort, respectively (both p < 0.05). The average total processing time for semi-automatic segmentation was significantly shorter than that for manual segmentation (35 s vs. 92 s, p < 0.001). The BCa radiomics model based on semi-automatic segmentation method had a similar diagnostic performance as that of manual segmentation, while being less time-consuming and requiring fewer manual interventions.

An Open-Source Deep Learning Algorithm for Efficient and Fully Automatic Analysis of the Choroid in Optical Coherence Tomography.

To develop an open-source, fully automatic deep learning algorithm, DeepGPET, for choroid region segmentation in optical coherence tomography (OCT) data. We used a dataset of 715 OCT B-scans (82 subjects, 115 eyes) from three clinical studies related to systemic disease. Ground-truth segmentations were generated using a clinically validated, semiautomatic choroid segmentation method, Gaussian Process Edge Tracing (GPET). We finetuned a U-Net with the MobileNetV3 backbone pretrained on ImageNet. Standard segmentation agreement metrics, as well as derived measures of choroidal thickness and area, were used to evaluate DeepGPET, alongside qualitative evaluation from a clinical ophthalmologist. DeepGPET achieved excellent agreement with GPET on data from three clinical studies (AUC = 0.9994, Dice = 0.9664; Pearson correlation = 0.8908 for choroidal thickness and 0.9082 for choroidal area), while reducing the mean processing time per image on a standard laptop CPU from 34.49 ± 15.09 seconds using GPET to 1.25 ± 0.10 seconds using DeepGPET. Both methods performed similarly according to a clinical ophthalmologist who qualitatively judged a subset of segmentations by GPET and DeepGPET, based on smoothness and accuracy of segmentations. DeepGPET, a fully automatic, open-source algorithm for choroidal segmentation, will enable researchers to efficiently extract choroidal measurements, even for large datasets. As no manual interventions are required, DeepGPET is less subjective than semiautomatic methods and could be deployed in clinical practice without requiring a trained operator. DeepGPET addresses the lack of open-source, fully automatic, and clinically relevant choroid segmentation algorithms, and its subsequent public release will facilitate future choroidal research in both ophthalmology and wider systemic health.

Sex- and age-based differences in fetal and early childhood hippocampus maturation: a cross-sectional and longitudinal analysis.

The hippocampus, essential for cognitive and affective processes, develops exponentially with differential trajectories seen in girls and boys, yet less is known about its development during early fetal life until early childhood. In a cross-sectional and longitudinal study, we examined the sex-, age-, and laterality-related developmental trajectories of hippocampal volumes in fetuses, infants, and toddlers associated with age. Third trimester fetuses (27-38weeks' gestational age), newborns (0-4weeks' postnatal age), infants (5-50weeks' postnatal age), and toddlers (2-3years postnatal age) were scanned with magnetic resonance imaging. A total of 133 datasets (62 female, postmenstrual age [weeks] M= 69.38, SD = 51.39, range = 27.6-195.3) were processed using semiautomatic segmentation methods. Hippocampal volumes increased exponentially during the third trimester and the first year of life, beginning to slow at approximately 2years. Overall, boys had larger hippocampal volumes than girls. Lateralization differences were evident, with left hippocampal growth beginning to plateau sooner than the right. This period of rapid growth from the third trimester, continuing through the first year of life, may support the development of cognitive and affective function during this period.

Semiautomated Detection of Early Infarct Signs on Noncontrast CT Improves Interrater Agreement.

Acute ischemic infarct identification on noncontrast computed tomography (NCCT) is highly variable between raters. A semiautomated method for segmentation of acute ischemic lesions on NCCT may improve interrater reliability. Patients with successful endovascular reperfusion from the DEFUSE 3 trial (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke) were included. We created relative NCCT (rNCCT) color-gradient overlays by comparing the density of a voxel on NCCT to the homologous region in the contralateral hemisphere. Regions with a relative hypodensity of at least 5% were visualized. We coregistered baseline and follow-up images. Two neuroradiologists and 6 nonradiologists segmented the acute ischemic lesion on the baseline scans with 2 methods: (1) manually outlining hypodense regions on the NCCT (unassisted segmentation) and (2) manually excluding areas deemed outside of the ischemic lesion on the rNCCT color map (rNCCT-assisted segmentation). Voxelwise interrater agreement was quantified using the Dice similarity coefficient and volumetric agreement between raters with the detection index (DI), defined as the true positive volume minus the false positive volume. From a total of 92, we included 61 patients. Median age was 59 (64-77), and 57% were female. Stroke onset was known in 39%. Onset to NCCT was median, 8.5 hours (7-11) and median 10 hours (8.4-11.5) in patients with known and unknown onset, respectively. Compared with unassisted NCCT segmentation, rNCCT-assisted segmentation increased the Dice similarity coefficient by >50% for neuroradiologists (Dice similarity coefficient, 0.38 versus 0.83; P<0.001) and nonradiologists (Dice similarity coefficient, 0.14 versus 0.84; P<0.001), and improved the DI among nonradiologists (mean improvement, 5.8 mL [95% CI, 3.1-8.5] mL, P<0.001) but not among neuroradiologists. The high variability of manual segmentations of the acute ischemic lesion on NCCT is greatly reduced using semiautomated rNCCT. The rNCCT map may therefore aid acute infarct detection and provide more reliable infarct estimates for clinicians with less experience.

A Segmentation Method of Serialized Human Body Slices Based on Matting Strategy and Skeleton Extraction.

In this paper, a semiautomatic image segmentation method for the serialized body slices of the Visible Human Project (VHP) is proposed. In our method, we first verified the effectiveness of the shared matting method for the VHP slices and utilized it to segment a single image. Then, to meet the need for the automatic segmentation of serialized slice images, a method based on the parallel refinement method and flood-fill method was designed. The ROI (region of interest) image of the next slice can be extracted by using the skeleton image of the ROI in the current slice. Utilizing this strategy, the color slice images of the Visible Human body can be continuously and serially segmented. This method is not complex but is rapid and automatic with less manual participation. The experimental results show that the primary organs of the Visible Human body can be accurately extracted.