Semi-automatic Segmentation Technique Research Articles

Quantitative MRI approach in the preoperative evaluation of a rare large-mass abdominal GIST tumor

Gastrointestinal stromal tumors (GISTs) are rare cancers originating from the gastrointestinal tract's interstitial cells of Cajal. This study explores the utility of quantitative Magnetic Resonance Imaging (MRI), particularly volumetric lesion segmentation, in the preoperative evaluation of large-mass abdominal GISTs. MRI offers high-resolution imaging without ionizing radiation, which is crucial for oncology patients requiring repeated scans. Advanced semi-automatic segmentation techniques were employed in this study from different MRI sequences to enhance precision in tumor boundary delineation and volumetric analysis, and aid personalized surgical planning. In addition, this study demonstrates that using different clinical imaging protocols and comparing volumetric data from CT and various MRI sequences yields consistent results with minimal volumetric error. Finally, integrating MRI data into real-time surgical navigation systems underscored the synergy between radiology, surgical oncology, and computer science, improving tumor margin assessment and reducing recurrence risk. This approach highlights a pivotal shift towards technology-enhanced healthcare, promising better patient outcomes.

One-Year Longitudinal Assessment of Patients With CMT1A Using Quantitative MRI.

Intramuscular fat fraction (FF) assessed using quantitative MRI (qMRI) has emerged as one of the few responsive outcome measures in CMT1A suitable for future clinical trials. This study aimed to identify the relevance of multiple qMRI biomarkers for tracking longitudinal changes in CMT1A and to assess correlations between MRI metrics and clinical parameters. qMRI was performed in CMT1A patients at 2 time points, a year apart, and various metrics were extracted from 3-dimensional volumes of interest at thigh and leg levels. A semiautomated segmentation technique was used, enabling the analysis of central slices and a larger 3D muscle volume. Metrics included proton density (PD), magnetization transfer ratio (MTR), and intramuscular FF. The sciatic and tibial nerves were also assessed. Disease severity was gauged using Charcot Marie Tooth Neurologic Score (CMTNSv2), Charcot Marie Tooth Examination Score, Overall Neuropathy Limitation Scale scores, and Medical Research Council (MRC) muscle strength. Twenty-four patients were included. FF significantly rose in the 3D volume at both thigh (+1.04% ± 2.19%, p = 0.041) and leg (+1.36% ± 1.87%, p = 0.045) levels. The 3D analyses unveiled a length-dependent gradient in FF, ranging from 22.61% ± 10.17% to 26.17% ± 10.79% at the leg level. There was noticeable variance in longitudinal changes between muscles: +3.17% ± 6.86% (p = 0.028) in the tibialis anterior compared with 0.37% ± 4.97% (p = 0.893) in the gastrocnemius medialis. MTR across the entire thigh volume showed a significant decline between the 2 time points -2.75 ± 6.58 (p = 0.049), whereas no significant differences were noted for the 3D muscle volume and PD. No longitudinal changes were observed in any nerve metric. Potent correlations were identified between FF and primary clinical measures: CMTNSv2 (ρ = 0.656; p = 0.001) and MRC in the lower limbs (ρ = -0.877; p < 0.001). Our results further support that qMRI is a promising tool for following up longitudinal changes in CMT1A patients, FF being the paramount MRI metric for both thigh and leg regions. It is crucial to scrutinize the postimaging data extraction methods considering that annual changes are minimal (around +1.5%). Given the varied FF distribution, the existence of a length-dependent gradient, and the differential fatty involution across muscles, 3D volume analysis appeared more suitable than single slice analysis.

Artificial intelligence vs. semi-automated segmentation for assessment of dental periapical lesion volume index score: A cone-beam CT study

IntroductionCone beam computed tomography periapical volume index (CBCTPAVI) is a categorisation tool to assess periapical lesion size in three-dimensions and predict treatment outcomes. This index was determined using a time-consuming semi-automatic segmentation technique. This study compared artificial intelligence (AI) with semi-automated segmentation to determine AI's ability to accurately determine CBCTPAVI score. MethodsCBCTPAVI scores for 500 tooth roots were determined using both the semi-automatic segmentation technique in three-dimensional imaging analysis software (Mimics Research™) and AI (Diagnocat™). A confusion matrix was created to compare the CBCTPAVI score by the AI with the semi-automatic segmentation technique. Evaluation metrics, precision, recall, F1-score (2×precision×recallprecision+recall), and overall accuracy were determined. ResultsIn 84.4 % (n = 422) of cases the AI classified CBCTPAVI score the same as the semi-automated technique. AI was unable to classify any lesion as index 1 or 2, due to its limitation in small volume measurement. When lesions classified as index 1 and 2 by the semi-automatic segmentation technique were excluded, the AI demonstrated levels of precision, recall and F1-score, all above 0.85, for indices 0, 3–6; and accuracy over 90 %. ConclusionsDiagnocat™ with its ability to determine CBCTPAVI score in approximately 2 min following upload of the CBCT could be an excellent and efficient tool to facilitate better monitoring and assessment of periapical lesions in everyday clinical practice and/or radiographic reporting. However, to assess three-dimensional healing of smaller lesions (with scores 1 and 2), further advancements in AI technologies are needed.

An evaluation of root resorption associated with the use of photobiomodulation during orthodontic treatment with clear aligners: a retrospective cohort pilot study.

To evaluate the change in tooth root volume using cone-beam computed tomography (CBCT) in a group of patients treated concurrently with clear aligners and an adjunctive photobiomodulation (PBM) device. This retrospective cohort pilot study included the records of 32 consecutively treated clear aligner patients (23 female, 9 male) from the private practice of one orthodontist. The PBM group (n = 16) used the device once per day for 5 minutes per arch and was compared with a matched control group (n = 16). A semiautomated segmentation technique was used to obtain tooth volume of anterior teeth from CBCT imaging prior to (T0) and during or immediately following (T1) orthodontic treatment with clear aligners. The change in root volume between time points was assessed. There was no statistically significant difference between the pre- and posttreatment root volumes of maxillary and mandibular anterior teeth, regardless of which intervention group the patient belonged to (P > .05). There was also no difference in the mean percentage change in root volume between clear aligner patients in this study who were treated with the PBM device compared with a matched control group (P > .05). Clear aligner patients in this study who changed their aligners every 3 to 5 days and used adjunctive photobiomodulation therapy did not experience clinically relevant orthodontically induced external root resorption. Due to the small sample size and measurement error in the root segmentation process, the results should be interpreted with caution.

Accelerating biomedical image segmentation using equilibrium optimization with a deep learning approach

&lt;abstract&gt; &lt;p&gt;Biomedical image segmentation is a vital task in the analysis of medical imaging, including the detection and delineation of pathological regions or anatomical structures within medical images. It has played a pivotal role in a variety of medical applications, involving diagnoses, monitoring of diseases, and treatment planning. Conventionally, clinicians or expert radiologists have manually conducted biomedical image segmentation, which is prone to human error, subjective, and time-consuming. With the advancement in computer vision and deep learning (DL) algorithms, automated and semi-automated segmentation techniques have attracted much research interest. DL approaches, particularly convolutional neural networks (CNN), have revolutionized biomedical image segmentation. With this motivation, we developed a novel equilibrium optimization algorithm with a deep learning-based biomedical image segmentation (EOADL-BIS) technique. The purpose of the EOADL-BIS technique is to integrate EOA with the Faster RCNN model for an accurate and efficient biomedical image segmentation process. To accomplish this, the EOADL-BIS technique involves Faster R-CNN architecture with ResNeXt as a backbone network for image segmentation. The region proposal network (RPN) proficiently creates a collection of a set of region proposals, which are then fed into the ResNeXt for classification and precise localization. During the training process of the Faster RCNN algorithm, the EOA was utilized to optimize the hyperparameter of the ResNeXt model which increased the segmentation results and reduced the loss function. The experimental outcome of the EOADL-BIS algorithm was tested on distinct benchmark medical image databases. The experimental results stated the greater efficiency of the EOADL-BIS algorithm compared to other DL-based segmentation approaches.&lt;/p&gt; &lt;/abstract&gt;

Development of a semi-automatic segmentation technique based on mean magnetic resonance imaging intensity thresholding for volumetric quantification of plexiform neurofibromas

Rationale and objectivesPlexiform neurofibromas (PNs) are peripheral nerve tumors that occur in 25–50 % of patients with neurofibromatosis type 1. PNs may have complex, diffused, and irregular shapes. The objective of this work was to develop a volumetric quantification method for PNs as clinical assessment is currently based on unidimensional measurement. Materials and methodsA semi-automatic segmentation technique based on mean magnetic resonance imaging (MRI) intensity thresholding (SSTMean) was developed and compared to a similar and previously published technique based on minimum image intensity thresholding (SSTMini). The performance (volume and computation time) of the two techniques was compared to manual tracings of 15 tumors of different locations, shapes, and sizes. Performance was also assessed using different MRI sequences. Reproducibility was assessed by inter-observer analysis. ResultsWhen compared to manual tracing, quantification performed with SSTMean was not significantly different (mean difference: 1.2 %), while volumes computed by SSTMini were significantly different (p < .0001, mean difference: 13.4 %). Volumes quantified by SSTMean were also significantly different than the ones assessed by SSTMini (p < .0001). Using SSTMean, volumes quantified with short TI inversion recovery, T1-, and T2-weighted imaging were not significantly different. Computation times used by SSTMean and SSTMini were significantly lower than for manual segmentation (p < .0001). The highest difference measured by two users was 8 cm3. ConclusionOur method showed accuracy compared to a current gold standard (manual tracing) and reproducibility between users. The refined segmentation threshold and the possibility to define multiple regions-of-interest to initiate segmentation may have contributed to its performance. The versatility and speed of our method may prove useful to better monitor volumetric changes in lesions of patients enrolled in clinical trials to assessing response to therapy.

Assessment of interobserver concordance in radiomic tools for lung nodule classification, with a focus on BRODERS and SILA

While CT lung cancer screening reduces lung cancer-specific mortality, there are remaining challenges. Radiomic tools promiss to address these challenges, however, they are subject to interobserver variability if semi-automated segmentation techniques are used. Herein we report interobserver variability for two validated radiomic tools, BRODERS (Benign versus aggRessive nODule Evaluation using Radiomic Stratification) and CANARY (Computer-Aided Nodule Assessment and Risk Yield). We retrospectively analyzed the CT images of 95 malignant lung nodules of the adenocarcinoma spectrum using BRODERS and CANARY. Cases were identified at Mayo Clinic (n = 45) and Vanderbilt University Medical Center and Nashville/Veteran Administration Tennessee Valley Health Care System (n = 50). Three observers with different training levels (medical student, internal medicine resident and thoracic radiology fellow) each performed lung nodule segmentation. All methods were carried out in accordance with relevant guidelines and regulations. Interclass correlation coefficients (ICC) of 0.77, 0.98 and 0.97 for the average nodule volume, BRODERS cancer probability and Score Indicative of Lesion Aggression (SILA) which summarizes the distribution of the CANARY exemplars indicated good to excellent reliability, respectively. The dice similarity coefficient was 0.79 and 0.81 for the data sets from the two institutions. BRODERS and CANARY are robust radiomics tools with excellent interobserver variability. These tools are simple and reliable regardless the observer/operator’s level of training.

White matter hyperintensities and smaller cortical thickness are associated with neuropsychiatric symptoms in neurodegenerative and cerebrovascular diseases

BackgroundNeuropsychiatric symptoms (NPS) are a core feature of most neurodegenerative and cerebrovascular diseases. White matter hyperintensities and brain atrophy have been implicated in NPS. We aimed to investigate the relative contribution of white matter hyperintensities and cortical thickness to NPS in participants across neurodegenerative and cerebrovascular diseases.MethodsFive hundred thirteen participants with one of these conditions, i.e. Alzheimer’s Disease/Mild Cognitive Impairment, Amyotrophic Lateral Sclerosis, Frontotemporal Dementia, Parkinson’s Disease, or Cerebrovascular Disease, were included in the study. NPS were assessed using the Neuropsychiatric Inventory – Questionnaire and grouped into hyperactivity, psychotic, affective, and apathy subsyndromes. White matter hyperintensities were quantified using a semi-automatic segmentation technique and FreeSurfer cortical thickness was used to measure regional grey matter loss.ResultsAlthough NPS were frequent across the five disease groups, participants with frontotemporal dementia had the highest frequency of hyperactivity, apathy, and affective subsyndromes compared to other groups, whilst psychotic subsyndrome was high in both frontotemporal dementia and Parkinson’s disease. Results from univariate and multivariate results showed that various predictors were associated with neuropsychiatric subsyndromes, especially cortical thickness in the inferior frontal, cingulate, and insula regions, sex(female), global cognition, and basal ganglia-thalamus white matter hyperintensities.ConclusionsIn participants with neurodegenerative and cerebrovascular diseases, our results suggest that smaller cortical thickness and white matter hyperintensity burden in several cortical-subcortical structures may contribute to the development of NPS. Further studies investigating the mechanisms that determine the progression of NPS in various neurodegenerative and cerebrovascular diseases are needed.

U-Net Architectures for Prostate Cancer Radiation Therapy

Prostate Cancer (PCa) was first diagnosed in 1853 as cirrhosis of the prostate gland. Silently asymptomatic, PCa is usually diagnosed with Digital Rectal Examination (DRE) and Prostate Specific Antigen (PSA) levels. Since the first treatment of an advanced prostatic malignancy with X-rays by Imbert and Imbert in 1904, External Beam Radiation Therapy (EBRT) is now a curative option for localised and locally advanced disease and a palliative option for the metastatic low-volume disease. With the introduction of computers in EBRT and better imaging techniques, volume delineation is still a very time-consuming task. While Deep Learning methods are urged, EBRT systems still rely on manual or semi-automatic segmentation techniques. The U-Net architecture was specially designed for medical image segmentation presenting promising results. This literature review gathers work using U-Net architectures, outlining methods, techniques and obtained outcomes as a potential foundation for an automated segmentation framework for PCa.

Assessment of immunotherapy response in intracranial malignancy using semi-automatic segmentation on magnetic resonance images.

To explore multi-aspect radiologic assessment of immunotherapy response in intracranial malignancies based on a semi-automatic segmentation technique, and to explore volumetric thresholds with good performance according to RECIST 1.1 thresholds. Patients diagnosed with intracranial malignancies and treated with immunotherapy were included retrospectively. In all MR images, target lesions were measured using a semi-automatic segmentation technique that could intelligently generate visual diagrams including RECIST 1.1, total volume, and max. 3D diameter. The changes in parameters were calculated for each patient after immunotherapy. The ROC curve was used to analyze the sensitivity and specificity of the size change of the legion. This was useful to find new volumetric thresholds with better efficiency in response assessment. The changes in total volume were assessed by conventional volumetric thresholds, while RECIST 1.1 thresholds were for the max. 3D diameter. A chi-square test was used to compare the concordance and diagnostic correlation between the response assessment results of the three criteria. A total of 20 cases (average age, 58 years; range, 23 to 84 years) and 58 follow-up MR examinations after immunotherapy were included in the analysis. The P-value of the chi-square test between RECIST 1.1 and total volume is 0 (P <0.05), same as that in RECIST 1.1 and max. 3D diameter. The kappa value of the former two was 0.775, and the kappa value for the latter two was 0.742. The above results indicate a significant correlation and good concordance for all three criteria. In addition, we also found that the volumetric assessment had the best sensitivity and specificity for the immunotherapy response in intracranial malignancies, with a PR threshold of -64.9% and a PD threshold of 21.4%. Radiologic assessment of immunotherapy response in intracranial malignancy can be performed by multiple criteria based on semi-automatic segmentation technique on MR images, such as total volume, max. 3D diameter and RECIST 1.1. In addition, new volumetric thresholds with good sensitivity and specificity were found by volumetric assessment.

Machine Learning Based Technique to Learn Hippocampal Atrophy from Axial MRI for Alzheimer's Disease Diagnosis

Hippocampus (HC) is one of the small brain components and its features majorly take part in diagnosing diseases such as Alzheimer and Dementia. The earlier detection of the size changes of HC leads to take preventive action against Alzheimer disease at initial stage. Thus the HC voxel quantification becomes essential to know the severity of the disease and thus induces computerized segmentation process. Several semi-automatic and automatic HC segmentation techniques proposed earlier. Though, it requires large memory space and high computational cost. This paper reduces the risk of searching a high configuration machine and reduces the cost by utilizing limited number of features. It is to be done by using some strategic features based on mathematical framework of wavelet, statistical features and gray level computations called level set. The features fed as input to the supervised machine learning model called back propagation neural network. A deep study conducted to train the net and analyzed in various views. The results were compared with the similar existing models which were using Random forest, Quicknat and deep learning. The proposed machine learning model produces the higher and similar dice scores of existing model. The validation of the proposed method yields 85% of dice score and 96% of sensitivity and 96% of specificity.

Sphericity of Periapical Lesion and Its Relation to the Novel CBCT Periapical Volume Index

BackgroundThe purpose of this study was to assess the sphericity of periapical lesions and its relation to the cone-beam computed tomography periapical volume index (CBCTPAVI). MethodsA total of 261 periapical lesions were assessed using cone-beam computed tomography images from InteleViewer. Three-dimensional analysis of the lesions was conducted using analytical imaging software Mimics Research. Lesion volume and surface area measurements were determined using the semiautomatic segmentation technique, and these measurements were then used to determine lesion sphericity and CBCTPAVI score. One-way analysis of variance with post hoc Tukey test was used to assess for differences in sphericity among CBCTPAVI groups. ResultsThe mean sphericity of periapical lesions was 62%. Periapical lesions with larger CBCTPAVI scores were significantly less spherical than lesions with smaller CBCTPAVI scores. ConclusionsPeriapical lesions of endodontic origin are mostly semi-spherical in their spread, and as CBCTPAVI score increases, sphericity decreases, indicating that larger lesions expand less uniformly compared with smaller lesions. Clinicians should be aware that lesions of increased volume have less sphericity, and are thus elongated or stretched in 1 or more anatomic plane. This information will assist clinicians in planning and performing periapical surgery and may aid in differential diagnosis of radiolucent jaw lesions.

Volume and shape changes of mandibular condyles in growing patients treated with fixed Class II appliances using CBCT

Objective: Evaluate the changes in the mandibular condyle volume and shape in growing Class II malocclusion patients, following the use of fixed functional Class II appliances (Forsus and Advacsync II). CBCT images were used to help understand which appliance might cause more changes. Methods: Twenty growing adolescent patients were randomly allocated to Two groups (Forsus and Advancesync II). A total of fourty CBCT images were taken pre- and posttreatment. The pre- and posttreatment CBCT images were segmented using a newly developed and validated semiautomatic condylar segmentation technique. and the quantitative assessment of the mean distance differences of the condyle surfaces was de using Iterative Closest Point technique. Results: A statistically significant increase in condylar volume was observed in both groups (Forsus and Advancesync II) and no significant differences in the magnitude of condylar volumes between both groups were found. There were no statistically significant changes in the mean distance differences of the condyles shape in both groups.Both groups (Forsus and Advancesync II) expressed different patterns of shape changes with no clear pattern associated with the treatment groups.

Comparative Analysis for the Distinction of Chromophobe Renal Cell Carcinoma from Renal Oncocytoma in Computed Tomography Imaging Using Machine Learning Radiomics Analysis.

Simple SummaryChromophobe renal cell carcinoma (ChRCC) is the 3rd most common subtype of renal cell carcinoma (RCC), which is difficult to differentiate from benign renal oncocytoma (RO) using conventional imaging techniques. The aim of this study was to differentiate chromophobe renal cell carcinoma and renal oncocytoma non-invasively using radiomics features extracted from pre-operative computed tomography (CT) scan images in combination with machine learning (ML) techniques for classification. This would help in providing “virtual biopsy” and may prevent unnecessary surgical resection for benign renal oncocytoma tumors.Background: ChRCC and RO are two types of rarely occurring renal tumors that are difficult to distinguish from one another based on morphological features alone. They differ in prognosis, with ChRCC capable of progressing and metastasizing, but RO is benign. This means discrimination of the two tumors is of crucial importance. Objectives: The purpose of this research was to develop and comprehensively evaluate predictive models that can discriminate between ChRCC and RO tumors using Computed Tomography (CT) scans and ML-Radiomics texture analysis methods. Methods: Data were obtained from 78 pathologically confirmed renal masses, scanned at two institutions. Data from the two institutions were combined to form a third set resulting in three data cohorts, i.e., cohort 1, 2 and combined. Contrast-enhanced scans were used and the axial cross-sectional slices of each tumor were extracted from the 3D data using a semi-automatic segmentation technique for both 2D and 3D scans. Radiomics features were extracted before and after applying filters and the dimensions of the radiomic features reduced using the least absolute shrinkage and selection operator (LASSO) method. Synthetic minority oversampling technique (SMOTE) was applied to avoid class imbalance. Five ML algorithms were used to train models for predictive classification and evaluated using 5-fold cross-validation. Results: The number of selected features with good model performance was 20, 40 and 6 for cohorts 1, 2 and combined, respectively. The best model performance in cohorts 1, 2 and combined had an excellent Area Under the Curve (AUC) of 1.00 ± 0.000, 1.00 ± 0.000 and 0.87 ± 0.073, respectively. Conclusions: ML-based radiomics signatures are potentially useful for distinguishing ChRCC and RO tumors, with a reliable level of performance for both 2D and 3D scanning.

3D driven hard tissue augmentation based on reverse planning Reconstruction of an advanced, horizonto-vertical alveolar ridge defect: A case report

This case report presents a novel, 3D-driven reverse planning for surgical rehabilitation of an advanced alveolar ridge defect. Utilizing a completely digital workflow, alveolar ridge augmentation could virtually be simulated thus simplifying the surgical procedure. In this case report the digitally planned treatment of a severe horizontovertical alveolar ridge defect is presented. Preoperative cone-beam computed tomography (CBCT) scan was segmented using a semi-automatic segmentation technique in an open-source medical image processing software (3D Slicer) to develop virtual 3D models of dental and alveolar structures. Intraoral scan, bite registration was also acquired to generate a digital wax-up. Consequently, prosthetically ideal implant positions could be planned in 3D. Based on future implant positions, adequate amount of hard tissue augmentation could be planned virtually. Individualized form of the barrier membrane could be determined, allowing the additive manufacturing of a membrane cutting template for shaping the barrier membrane during surgery. The combination of a non-resorbable barrier membrane, tenting screws, and composite graft (50% xenograft, 50% autogenous bone) was utilized. A follow-up CBCT scan was taken after 9 months to determine volumetric and 3D morphological changes. After 9 months, a hard tissue gain of 0.81 cm 3 could be observed. During the 6-9 month healing period membrane exposition did not occur. Virtual planning and subsequent surgical simulation on 3D models is a novel method in “prosthetically driven” augmentation potentially reducing the duration of surgery and postoperative complications. However, further studies are necessary to drive any more conclusions.

Validating a Semi-Automated Technique for Segmenting Femoral Articular Cartilage on Ultrasound Images.

ObjectiveTo validate a semi-automated technique to segment ultrasound-assessed femoral cartilage without compromising segmentation accuracy to a traditional manual segmentation technique in participants with an anterior cruciate ligament injury (ACL).DesignWe recruited 27 participants with a primary unilateral ACL injury at a pre-operative clinic visit. One investigator performed a transverse suprapatellar ultrasound scan with the participant’s ACL injured knee in maximum flexion. Three femoral cartilage ultrasound images were recorded. A single expert reader manually segmented the femoral cartilage cross-sectional area in each image. In addition, we created a semi-automatic program to segment the cartilage using a random walker-based method. We quantified the average cartilage thickness and echo-intensity for the manual and semi-automated segmentations. Intraclass correlation coefficients (ICC2,k) and Bland-Altman plots were used to validate the semi-automated technique to the manual segmentation for assessing average cartilage thickness and echo-intensity. A dice correlation coefficient was used to quantify the overlap between the segmentations created with the semi-automated and manual techniques.ResultsFor average cartilage thickness, there was excellent reliability (ICC2,k = 0.99) and a small mean difference (+0.8%) between the manual and semi-automated segmentations. For average echo-intensity, there was excellent reliability (ICC2,k = 0.97) and a small mean difference (−2.5%) between the manual and semi-automated segmentations. The average dice correlation coefficient between the manual segmentation and semi-automated segmentation was 0.90, indicating high overlap between techniques.ConclusionsOur novel semi-automated segmentation technique is a valid method that requires less technical expertise and time than manual segmentation in patients after ACL injury.

Magnetic resonance imaging quantitative analysis of knee joint injury and cartilage before and after long-distance march and training for college students

Objective: To assess the effects of long-distance march and training on acute knee injury and knee cartilage sub-regions of college students using quantitatively magnetic resonance imaging analysis. Methods: Twenty-seven young male students from freshman classes in the Army Military Medical University were enrolled in September 2019, aged from 17 to 20 (19.48±0.14) years, participated in the whole 8-day, 240 km long-distance march and training. Three-dimensional quantitative MRI was performed on the right knee using high-field MRI before (baseline) and 1 day after (follow-up) march. The assessment indexes included: meniscus and cartilage injury(5-point scale), bone marrow and ligament injury, and joint effusion(3-point scale). Using semi-automatic cartilage segmentation and 3D data post-processing techniques, a total of 21 sub-regions of cartilage volume and thickness were measured in the medial and lateral femur, medial and lateral tibia. Paired-samples t-test was used to compare the changes in quantitative cartilage indices of the knee joint before and after march. Results: In terms of acute knee injuries, medial and lateral meniscus injuries (grade 1-3) occurred in 8 and 9 college students, respectively compared with those before march. Anterior cruciate ligaments injury occurred in 4 college students and developed from grade 0 to grade 1. Bone marrow edema occurred in 10 students and developed from grade 0 to grade 2, and in 5 students from grade 0 to grade 1. Joint effusion occurred in 5 college students and developed from grade 1 to grade 2 (all P<0.05). In terms of quantitative analysis of cartilage subregion of knee joint, the volume of central region of femoral pulley increased [(1.84±0.32) mm3 vs (1.67±0.29) mm3] and the volume of central region of medial femoral condyle decreased [(1.18±0.21) mm3 vs (1.26±0.17) mm3] compared with that before march (all P<0.05); The cartilage thickness of 11 cartilage subregion [(1.37±0.27) mm vs (1.53±0.18) mm], [(1.42±0.25) mm vs (1.54±0.17) mm], [(1.53±0.20) mm vs (1.62±0.20) mm], [(1.72±0.28) mm vs (1.83±0.28) mm], [(1.84±0.45) mm vs (2.04±0.42) mm], [(2.20±0.58) mm vs (2.46±0.50) mm], [(1.74±0.19) mm vs (1.85±0.21) mm] [(1.45±0.21) mm vs (1.58±0.16) mm], [(1.81±0.22) mm vs (1.91±0.15) mm], [(1.44±0.13) mm vs (1.53±0.15) mm] was thinner than that before march (all P<0.05). The T2 values of 7 cartilage subregion [(40.57±26.23) ms vs (67.10±47.46) ms], [(80.10±20.56) ms vs (98.42±23.58) ms], [(87.92±24.95) ms vs (108.84±29.24) ms], [(50.49±19.18) ms vs (76.97±37.16) ms], [(38.89±15.82) ms vs (69.70±40.16) ms] [(55.84±24.53) ms vs (106.35±50.01) ms] and [(72.38±36.64) ms vs (105.31±39.34) ms] were lower than those before march, while the T2 values of the two subregions of patellar cartilage [(102.13±44.47) ms vs (72.20±28.37) ms], [(97.42±44.86) ms vs (76.67±51.64) ms] were higher than those before march (P<0.05). There was no significant difference in other cartilage subareas (P>0.05). Conclusions: Long distance hiking will lead to acute injury of knee joint of young college students. The thickness of cartilage subregion of knee joint showed a thinning trend as a whole, while the volume and T2 values showed different trends.

Lobar pulmonary perfusion quantification with dual-energy CT angiography: Interlobar variability and relationship with regional clot burden in pulmonary embolism

PurposeSemi-automated lobar segmentation tools enable an anatomical assessment of regional pulmonary perfusion with Dual-Energy CTA (DE-CTA). We aimed to quantify lobar pulmonary perfusion with DE-CTA, analyze the perfusion distribution among the pulmonary lobes in subjects without cardiopulmonary diseases and assess the correlation between lobar perfusion and regional endoluminal clots in patients with acute pulmonary embolism (PE). MethodsWe evaluated 151 consecutive subjects with suspected PE and without cardiopulmonary comorbidities. DE-CTA derived perfused blood volume (PBV) of each pulmonary lobe was measured applying a semi-automated lobar segmentation technique. In patients with PE, blood clot location was assessed, and CT-based vascular obstruction index of each lobe (CTOIlobe) was calculated and classified into three groups: CTOIlobe= 0, low CTOIlobe (1–50%) and high CTOIlobe (>50%). ResultsAmong patients without PE (103/151, 68.2%), median lobar PBV was 13.7% (IQR 10.2–18.0%); the right middle lobe presented lower PBV when compared to all the other lobes (p < .001). In patients with PE (48/151, 31.8%), lobar PBV was 12.6% (IQR 9.6–15.7%), 13.7% (IQR 10.1–16.7%) and 6.5% (IQR 5.1–10.2%) in the lobes with CTOIlobe= 0, low CTOIlobe and high CTOIlobe scores, respectively, with a significantly decreased PBV in the lobes with high CTOIlobe score (p < .001). ROC analysis of lobar PBV for prediction of high CTOIlobe score revealed AUC of 0.847 (95%CI 0.785–0.908). ConclusionPulmonary perfusion was heterogeneously distributed along the pulmonary lobes in patients without cardiopulmonary diseases. In patients with PE, the lobes with high vascular obstruction score (CTOIlobe> 50%) presented a decreased lobar perfusion.

Semi-Supervised Segmentation of Renal Pathology: An Alternative to Manual Segmentation and Input to Deep Learning Training.

Kidney biopsy interpretation is the gold standard for the diagnosis and prognosis for kidney disease. Pathognomonic diagnosis hinges on the correct assessment of different structures within a biopsy that is manually visualized and interpreted by a renal pathologist. This laborious undertaking has spurred attempts to automate the process, offloading the consumption of temporal resources. Segmentation of kidney structures, specifically, the glomeruli, tubules, and interstitium, is a precursory step for disease classification problems. Translating renal disease decision making into a deep learning model for diagnostic and prognostic classification also relies on adequate segmentation of structures within the kidney biopsy. This study showcases a semi-automated segmentation technique where the user defines starting points for glomeruli in kidney biopsy images of both healthy normal and diabetic kidney disease stained with Nile Red that are subsequently partitioned into four areas: background, glomeruli, tubules and interstitium. Five of 30 biopsies that were segmented using the semi-automated method were randomly selected and the regions of interest were compared to the manual segmentation of the same images. Dice Similarity Coefficients (DSC) between the methods showed excellent agreement; Healthy (glomeruli: 0.92, tubules: 0.86, intersititium: 0.78) and diabetic nephropathy: (glomeruli: 0.94, tubules: 0.80, intersititium: 0.80). To our knowledge this is the first semi-automated segmentation algorithm performed with human renal biopsies stained with Nile Red. Utility of this methodology includes further image processing within structures across disease states based on biological morphological structures. It can also be used as input into a deep learning network to train semantic segmentation and input into a deep learning algorithm for classification of disease states.

A Semiautomatic Multi-Label Color Image Segmentation Coupling Dirichlet Problem and Colour Distances.

Image segmentation is an essential but critical component in low level vision, image analysis, pattern recognition, and now in robotic systems. In addition, it is one of the most challenging tasks in image processing and determines the quality of the final results of the image analysis. Colour based segmentation could hence offer more significant extraction of information as compared to intensity or texture based segmentation. In this work, we propose a new local or global method for multi-label segmentation that combines a random walk based model with a direct label assignment computed using a suitable colour distance. Our approach is a semi-automatic image segmentation technique, since it requires user interaction for the initialisation of the segmentation process. The random walk part involves a combinatorial Dirichlet problem for a weighted graph, where the nodes are the pixel of the image, and the positive weights are related to the distances between pixels: in this work we propose a novel colour distance for computing such weights. In the random walker model we assign to each pixel of the image a probability quantifying the likelihood that the node belongs to some subregion. The computation of the colour distance is pursued by employing the coordinates in a colour space (e.g., RGB, XYZ, YCbCr) of a pixel and of the ones in its neighbourhood (e.g., in a 8–neighbourhood). The segmentation process is, therefore, reduced to an optimisation problem coupling the probabilities from the random walker approach, and the similarity with respect the labelled pixels. A further investigation involves an adaptive preprocess strategy using a regression tree for learning suitable weights to be used in the computation of the colour distance. We discuss the properties of the new method also by comparing with standard random walk and means approaches. The experimental results carried on the White Blood Cell (WBC) dataset and GrabCut datasets show the remarkable performance of the proposed method in comparison with state-of-the-art methods, such as normalised random walk and normalised lazy random walk, with respect to segmentation quality and computational time. Moreover, it reveals to be very robust with respect to the presence of noise and to the choice of the colourspace.