Efficacy of Radiomics in Predicting Oncologic Outcome of Liver-Directed Combined Radiotherapy in Locally Advanced Hepatocellular Carcinoma.

PURPOSE: We investigated whether radiomic features extracted from three-phase dynamic contrast-enhanced computed tomography (CECT) can be used to predict clinical outcomes, including objective treatment response (OR) and in-field failure-free survival rate (IFFR), in patients with hepatocellular carcinoma (HCC) who received liver-directed combined radiotherapy (LD-CRT). METHODS: We included 409 patients, and they were randomly divided into training (n = 307) and validation (n = 102) cohorts. For radiomics models, we extracted 116 radiomic features from the region of interest on the CECT images. Significant clinical prognostic factors are identified to predict the OR and IFFR in the clinical models. We developed clinical models, radiomics models, and a combination of both features (CCR model). RESULTS: Among the radiomic models evaluated for OR, the OR-PVP-Peri-1cm model showed favorable predictive performance with an area under the curve (AUC) of 0.647. The clinical model showed an AUC of 0.729, whereas the CCR model showed better performance (AUC 0.759). For the IFFR, the IFFR-PVP-Peri-1cm model showed an AUC of 0.673, clinical model showed 0.687, and the CCR model showed 0.736. We also developed and validated a prognostic nomogram based on CCR models. CONCLUSION: In predicting the OR and IFFR in patients with HCC undergoing LD-CRT, CCR models performed better than clinical and radiomics models. Moreover, the constructed nomograms based on these models may provide valuable information on the prognosis of these patients.

Automated lung tumor segmentation robust to various tumor sizes using a consistency learning-based multi-scale dual-attention network.

BACKGROUND: It is often difficult to automatically segment lung tumors due to the large tumor size variation ranging from less than 1 cm to greater than 7 cm depending on the T-stage. OBJECTIVE: This study aims to accurately segment lung tumors of various sizes using a consistency learning-based multi-scale dual-attention network (CL-MSDA-Net). METHODS: To avoid under- and over-segmentation caused by different ratios of lung tumors and surrounding structures in the input patch according to the size of the lung tumor, a size-invariant patch is generated by normalizing the ratio to the average size of the lung tumors used for the training. Two input patches, a size-invariant patch and size-variant patch are trained on a consistency learning-based network consisting of dual branches that share weights to generate a similar output for each branch with consistency loss. The network of each branch has a multi-scale dual-attention module that learns image features of different scales and uses channel and spatial attention to enhance the scale-attention ability to segment lung tumors of different sizes. RESULTS: In experiments with hospital datasets, CL-MSDA-Net showed an F1-score of 80.49%, recall of 79.06%, and precision of 86.78%. This resulted in 3.91%, 3.38%, and 2.95% higher F1-scores than the results of U-Net, U-Net with a multi-scale module, and U-Net with a multi-scale dual-attention module, respectively. In experiments with the NSCLC-Radiomics datasets, CL-MSDA-Net showed an F1-score of 71.7%, recall of 68.24%, and precision of 79.33%. This resulted in 3.66%, 3.38%, and 3.13% higher F1-scores than the results of U-Net, U-Net with a multi-scale module, and U-Net with a multi-scale dual-attention module, respectively. CONCLUSIONS: CL-MSDA-Net improves the segmentation performance on average for tumors of all sizes with significant improvements especially for small sized tumors.

Elevated aerial temperature modulates digestive enzyme activities in Mytilus californianus.

The marine intertidal mussel Mytilus californianus aggregates to form beds along the Pacific shores of North America. As a sessile organism it must cope with fluctuations in temperature during low-tide aerial exposure, which elevates maintenance costs and negatively affects its overall energy budget. The function of its digestive gland is to release enzymes that break apart ingested polymers for subsequent nutrient absorption. The effects of elevated aerial warming acclimation on the functioning of digestive gland enzymes are not well studied. In this study we asked whether digestive gland carbohydases and proteases could be overstimulated in warm condition to possibly mitigate the costs related to the heat-shock response. We compared mussels acclimated to a + 9 °C heat-shock during daily low-tide aerial exposure to mussels acclimated to isothermal tidal conditions in a simulated intertidal system. The results showed fairly consistent activities of cellulase, trypsin, and amino-peptidase across tidal variation and between thermal treatments; however, amylase activity was lower in warmed versus cool mussels across low and high-tide. We also observed the expression of heat-shock genes in gill tissue during warm tidal conditions, suggestive that moderate temperatures during aerial exposure can induce a stress response.

Inter-Slice Resolution Improvement Using Convolutional Neural Network with Orbital Bone Edge-Aware in Facial CT Images.

The 3D modeling of orbital bones in facial CT images is essential to provide a customized implant for reconstructions of orbit and related structures during surgery. However, 3D models of the orbital bone show an aliasing effect and disconnected thin bone in the inter-slice direction because the slice thickness is two to three times larger than the pixel spacing. To improve the inter-slice resolution of facial CT images, we propose a method based on a 2D convolutional neural network (CNN) that uses the spatial information on the sagittal and axial planes and the orbital bone edge-aware (OBE) loss. First, intermediate slices are generated on the sagittal plane. Second, the generated intermediate slices are transformed to an axial image, which is then compared with the original axial image. To generate intermediate slices with an accurate orbital bone structure, the OBE loss considering the orbital bone structure on the sagittal and axial planes is used. To improve the perceptual quality of the generated intermediate slices, the feature map difference loss is additionally used on the axial plane. In the experiment, the proposed method showed the best performance among bilinear and bicubic interpolations, 3D SRGAN, and a 2D CNN-based method. Experimental results confirmed that the proposed method can generate intermediate slices with clear edges of thin bones as well as cortical bones on both the sagittal and the axial plane.

Lung tumor segmentation using dual-coupling net with shape prior based on lung and mediastinal window images from chest CT images.

BACKGROUND: Volumetric lung tumor segmentation is difficult due to the diversity of the sizes, locations and shapes of lung tumors, as well as the similarity in the intensity with surrounding tissue structures. OBJECTIVE: We propose a dual-coupling net for accurate lung tumor segmentation in chest CT images regardless of sizes, locations and shapes of lung tumors.METHODSTo extract shape information from lung tumors and use it as shape prior, three-planar images including axial, coronal, and sagittal planes are trained on 2D-Nets. Two types of window images, lung and mediastinal window images, are trained on 2D-Nets to distinguish lung tumors from the thoracic region and to better separate the boundaries of lung tumors from adjacent tissue structures. To prevent false-positive outliers to adjacent structures and to consider the spatial information of lung tumors, pairs of tumor volume-of-interest (VOI) and tumor shape prior are trained on 3D-Net.RESULTSIn the first experiment, the dual-coupling net had the highest Dice Similarity Coefficient (DSC) of 75.7%, considering the shape prior as well as mediastinal window images to prevent the leakage of adjacent structures while maintaining the shape of the lung tumor, with 18.23% p, 3.7% p, 1.1% p, and 1.77% p higher DSCs than in the 2D-Net, 2.5D-Net, 3D-Net, and single-coupling net results, respectively. In the second experiment with annotations for two clinicians, the dual-coupling net showed outcomes of 67.73% and 65.07% regarding the DSC for each annotation. In the third experiment, the dual-coupling net showed 70.97% for the DSC.CONCLUSIONSThe dual-coupling net enables accurate segmentation by distinguishing lung tumors from surrounding tissue structures and thus yields the highest DSC value.

Prediction of Two-Year Recurrence-Free Survival in Operable NSCLC Patients Using Radiomic Features from Intra- and Size-Variant Peri-Tumoral Regions on Chest CT Images.

To predict the two-year recurrence-free survival of patients with non-small cell lung cancer (NSCLC), we propose a prediction model using radiomic features of the inner and outer regions of the tumor. The intratumoral region and the peritumoral regions from the boundary to 3 cm were used to extract the radiomic features based on the intensity, texture, and shape features. Feature selection was performed to identify significant radiomic features to predict two-year recurrence-free survival, and patient classification was performed into recurrence and non-recurrence groups using SVM and random forest classifiers. The probability of two-year recurrence-free survival was estimated with the Kaplan-Meier curve. In the experiment, CT images of 217 non-small-cell lung cancer patients at stages I-IIIA who underwent surgical resection at the Veterans Health Service Medical Center (VHSMC) were used. Regarding the classification performance on whole tumors, the combined radiomic features for intratumoral and peritumoral regions of 6 mm and 9 mm showed improved performance (AUC 0.66, 0.66) compared to T stage and N stage (AUC 0.60), intratumoral (AUC 0.64) and peritumoral 6 mm and 9 mm classifiers (AUC 0.59, 0.62). In the assessment of the classification performance according to the tumor size, combined regions of 21 mm and 3 mm were significant when predicting outcomes compared to other regions of tumors under 3 cm (AUC 0.70) and 3 cm~5 cm (AUC 0.75), respectively. For tumors larger than 5 cm, the combined 3 mm region was significant in predictions compared to the other features (AUC 0.71). Through this experiment, it was confirmed that peritumoral and combined regions showed higher performance than the intratumoral region for tumors less than 5 cm in size and that intratumoral and combined regions showed more stable performance than the peritumoral region in tumors larger than 5 cm.

Multidimensional profiles of parent involvement: Antecedents and impact on student engagement.

BACKGROUND: Parent involvement in school is a consistent predictor of educational success. However, research has been inconsistent in addressing how parent involvement ought to be defined and measured, which has led to varied findings across schools and educational systems. AIMS: Attending to the multidimensionality of the construct, this study adopted a person-centred approach to identify subpopulations of school-based parent involvement. Subsequently, profile differences were investigated in relation to student engagement and three antecedent variables (gender, socio-economic status, and authoritative parenting). SAMPLE: Data were obtained from primary (10-year old; N = 4,284) and secondary (14-year old; N = 3,346) school students in Singapore. METHODS: Latent profile analysis was conducted on student-rated surveys of multiple parent involvement behaviours in school and their perceptions. Subsequently, the manual BCH method was employed to concurrently model covariates and outcomes on the latent profile model. Pairwise comparisons between profiles were examined for statistical significance. RESULTS: Consistent across both cohorts, four distinct profiles emerged that revealed high, moderate, selective, and low parent involvement patterns. High parent involvement reflected high ratings across multiple activities, combined with positive perceptions of parental involvement. These profiles differed significantly in terms of their antecedent characteristics, particularly, authoritative parenting, and in relation to their impact on student engagement. CONCLUSION: Results from this study clarify relations between multi-faceted dimensions of parent involvement in school. Additionally, there is a case for continued school-family partnerships among secondary students as students remain academically engaged when parents are involved in school and students relate positively to their involvement.

Automatic Meniscus Segmentation Using Adversarial Learning-Based Segmentation Network with Object-Aware Map in Knee MR Images.

Meniscus segmentation from knee MR images is an essential step when analyzing the length, width, height, cross-sectional area, surface area for meniscus allograft transplantation using a 3D reconstruction model based on the patient's normal meniscus. In this paper, we propose a two-stage DCNN that combines a 2D U-Net-based meniscus localization network with a conditional generative adversarial network-based segmentation network using an object-aware map. First, the 2D U-Net segments knee MR images into six classes including bone and cartilage with whole MR images at a resolution of 512 × 512 to localize the medial and lateral meniscus. Second, adversarial learning with a generator based on the 2D U-Net and a discriminator based on the 2D DCNN using an object-aware map segments the meniscus into localized regions-of-interest with a resolution of 64 × 64. The average Dice similarity coefficient of the meniscus was 85.18% at the medial meniscus and 84.33% at the lateral meniscus; these values were 10.79%p and 1.14%p, and 7.78%p and 1.12%p higher than the segmentation method without adversarial learning and without the use of an object-aware map with the Dice similarity coefficient at the medial meniscus and lateral meniscus, respectively. The proposed automatic meniscus localization through multi-class can prevent the class imbalance problem by focusing on local regions. The proposed adversarial learning using an object-aware map can prevent under-segmentation by repeatedly judging and improving the segmentation results, and over-segmentation by considering information only from the meniscus regions. Our method can be used to identify and analyze the shape of the meniscus for allograft transplantation using a 3D reconstruction model of the patient's unruptured meniscus.

Classification of focal liver lesions in CT images using convolutional neural networks with lesion information augmented patches and synthetic data augmentation.

PURPOSE: We propose a deep learning method that classifies focal liver lesions (FLLs) into cysts, hemangiomas, and metastases from portal phase abdominal CT images. We propose a synthetic data augmentation process to alleviate the class imbalance and the Lesion INformation Augmented (LINA) patch to improve the learning efficiency. METHODS: A dataset of 502 portal phase CT scans of 1,290 FLLs was used. First, to alleviate the class imbalance and to diversify the training data patterns, we suggest synthetic training data augmentation using DCGAN-based lesion mask synthesis and pix2pix-based mask-to-image translation. Second, to improve the learning efficiency of convolutional neural networks (CNNs) for the small lesions, we propose a novel type of input patch termed the LINA patch to emphasize the lesion texture information while also maintaining the lesion boundary information in the patches. Third, we construct a multi-scale CNN through a model ensemble of ResNet-18 CNNs trained on LINA patches of various mini-patch sizes. RESULTS: The experiments demonstrate that (a) synthetic data augmentation method shows characteristics different but complementary to those in conventional real data augmentation in augmenting data distributions, (b) the proposed LINA patches improve classification performance compared to those by existing types of CNN input patches due to the enhanced texture and boundary information in the small lesions, and (c) through an ensemble of LINA patch-trained CNNs with different mini-patch sizes, the multi-scale CNN further improves overall classification performance. As a result, the proposed method achieved an accuracy of 87.30%, showing improvements of 10.81%p and 15.0%p compared to the conventional image patch-trained CNN and texture feature-trained SVM, respectively. CONCLUSIONS: The proposed synthetic data augmentation method shows promising results in improving the data diversity and class imbalance, and the proposed LINA patches enhance the learning efficiency compared to the existing input image patches.

The observation of starch digestion in blue mussel Mytilus galloprovincialis exposed to microplastic particles under varied food conditions.

Microplastic continues to be an environmental concern, especially for filter feeding bivalves known to ingest these particles. It is important to understand the effects of microplastic particles on the physiological performance of these bivalves and many studies have investigated their impact on various physiological processes. This study investigated the effects of microplastic (10 µm) on digestive enzyme (amylase) activity of Mytilus galloprovincialis at 55,000 and 110,000 microplastic particles/L under laboratory conditions. Additionally, our study measured the expression of an isoform of Hsp70 in the gills to assess whether or not these particles may cause protein denaturation. Results revealed that this regime negatively affect the ability of M. galloprovincialis to digest starch under high food conditions but not low food conditions. Exposure to extreme levels of microplastic raised amylase activity. Furthermore, Hsp70 transcript abundance was not elevated in treatment mussels. These results show that mussels may be resilient to current microplastic pollution levels in nature.

Radiomics analysis of contrast-enhanced CT for classification of hepatic focal lesions in colorectal cancer patients: its limitations compared to radiologists.

OBJECTIVE: To evaluate diagnostic performance of a radiomics model for classifying hepatic cyst, hemangioma, and metastasis in patients with colorectal cancer (CRC) from portal-phase abdominopelvic CT images. METHODS: This retrospective study included 502 CRC patients who underwent contrast-enhanced CT and contrast-enhanced liver MRI between January 2005 and December 2010. Portal-phase CT images of training (n = 386) and validation (n = 116) cohorts were used to develop a radiomics model for differentiating three classes of liver lesions. Among multiple handcrafted features, the feature selection was performed using ReliefF method, and random forest classifiers were used to train the selected features. Diagnostic performance of the developed model was compared with that of four radiologists. A subgroup analysis was conducted based on lesion size. RESULTS: The radiomics model demonstrated significantly lower overall and hemangioma- and metastasis-specific polytomous discrimination index (PDI) (overall, 0.8037; hemangioma-specific, 0.6653; metastasis-specific, 0.8027) than the radiologists (overall, 0.9622-0.9680; hemangioma-specific, 0.9452-0.9630; metastasis-specific, 0.9511-0.9869). For subgroup analysis, the PDI of the radiomics model was different according to the lesion size (< 10 mm, 0.6486; ≥ 10 mm, 0.8264) while that of the radiologists was relatively maintained. For classifying metastasis from benign lesions, the radiomics model showed excellent diagnostic performance, with an accuracy of 84.36% and an AUC of 0.9426. CONCLUSION: Albeit inferior to the radiologists, the radiomics model achieved substantial diagnostic performance when differentiating hepatic lesions from portal-phase CT images of CRC patients. This model was limited particularly to classifying hemangiomas and subcentimeter lesions. KEY POINTS: • Albeit inferior to the radiologists, the radiomics model could differentiate cyst, hemangioma, and metastasis with substantial diagnostic performance using portal-phase CT images of colorectal cancer patients. • The radiomics model demonstrated limitations especially in classifying hemangiomas and subcentimeter liver lesions.

CT quantification of the heterogeneity of fibrosis boundaries in idiopathic pulmonary fibrosis.

OBJECTIVES: To quantify the heterogeneity of fibrosis boundaries in idiopathic pulmonary fibrosis (IPF) using the Gaussian curvature analysis for evaluating disease severity and predicting survival. METHODS: We retrospectively included 104 IPF patients and 52 controls who underwent baseline chest CT scans. Normal lungs below - 500 HU were segmented, and the boundary was three-dimensionally reconstructed using in-house software. Gaussian curvature analysis provided histogram features on the heterogeneity of the fibrosis boundary. We analyzed the correlations between histogram features and the gender-age-physiology (GAP) and CT fibrosis scores. We built a regression model to predict diffusing capacity of carbon monoxide (DLCO) using the histogram features and calculated the modified GAP (mGAP) score by replacing DLCO with the predicted DLCO. The performances of the GAP, CT-GAP, and mGAP scores were compared using 100 repeated random-split sets. RESULTS: Patients with moderate-to-severe IPF had more numerous Gaussian curvatures at the fibrosis boundary, lower uniformity, and lower 10th to 30th percentiles of Gaussian curvature than controls or patients with mild IPF (all p < 0.0033). The 20th percentile was most significantly correlated with the GAP score (r = - 0.357; p < 0.001) and the CT fibrosis score (r = - 0.343; p = 0.001). More numerous Gaussian curvatures, higher entropy, lower uniformity, and 10th to 30th percentiles (p < 0.001-0.041) were associated with mortality. The mGAP score was comparable to the GAP and CT-GAP scores for survival prediction (mean C-indices, 0.76 vs. 0.79 vs. 0.77, respectively). CONCLUSIONS: Gaussian curvatures of fibrosis boundaries became more heterogeneous as the disease progressed, and heterogeneity was negatively associated with survival in IPF. KEY POINTS: • Gaussian curvature of the fibrotic lung boundary was more heterogeneous in patients with moderate-to-severe IPF than those with mild IPF or normal controls. • The 20th percentile of the Gaussian curvature of the fibrosis boundary was linearly correlated with the GAP score and the CT fibrosis score. • A modified GAP score that replaced the diffusing capacity of carbon monoxide with a composite measure using histogram features of the Gaussian curvature of the fibrosis boundary showed a comparable ability to predict survival to both the GAP and the CT-GAP score.

Three-dimensional computer modeling of standard orbital mean shape in Asians.

Although some data for western norms in orbit shape were reported, the standard norms for Asian orbits were not established yet. The data would be very valuable for the various surgical procedures as well as the production of the appropriate instruments and implants. Therefore, we suggest a Korean orbit mean shape model based on the three-dimensional computer modeling, which includes the analysis of the various parameters with the calculated average value, thereby providing a standard mean shape orbital model that could be used for the Asian patients' orbital surgeries. This paper would be the first literature that provides the standard orbit model for Asians. We developed orbit-specific computer software (AMC-SWUⓇ) for the production of an orbit mean shape model. The production steps included semi-automatic segmentation, shape reconstruction, statistical shape model generation, and mean shape and variance model production. The study included records of 48 male and 48 female patients who met the inclusion criteria. Three-dimensional facial bone computed tomography (CT) images of 96 patients were obtained, and these images were used to produce a representative mean shape model. The mean models had vertical dimensions of 36.93 and 35.11 mm, horizontal dimensions of 38.49 and 36.79 mm, and rim dimensions of 45.76 and 42.90 mm for males and females, respectively. We developed a realistic, visualized three-dimensional Korean orbit mean shape model and compared its parameters with calculated values. There is a variance in orbital dimensions between the sexes and the orbital changes with age. We also demonstrated orbital anatomic differences between ethnic groups.

Author Correction: Inspiratory Lung Expansion in Patients with Interstitial Lung Disease: CT Histogram Analyses.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Effect of CT Reconstruction Algorithm on the Diagnostic Performance of Radiomics Models: A Task-Based Approach for Pulmonary Subsolid Nodules.

OBJECTIVE: We investigated whether the diagnostic performance of machine learning-based radiomics models for the discrimination of invasive pulmonary adenocarcinomas (IPAs) among subsolid nodules (SSNs) was affected by the proportion of images reconstructed with filtered back projection (FBP) and model-based iterative reconstruction (MBIR) in datasets used for feature extraction. MATERIALS AND METHODS: This retrospective study included 60 patients (23 men and 37 women; mean age, 61.4 years) with 69 SSNs (54 part-solid and 15 pure ground-glass nodules). Preoperative CT scans were reconstructed with both FBP and MBIR. A total of 860 radiomics features were obtained from the entire nodule volume, and 70 resampled nodule datasets with an increasing proportion of nodules with MBIR-derived features (from 0/69 to 69/69) were prepared. After feature selection using neighborhood component analysis, support vector machines (SVMs) and an ensemble model were used as classifiers for the differentiation of IPAs. The diagnostic performances of all blending proportions of reconstruction algorithms were calculated and analyzed. RESULTS: The ROC AUC and the diagnostic accuracy of the radiomics models decreased significantly as the number of nodules with MBIR-derived features increased, and this relationship followed cubic functions (R2 = 0.993 and 0.926 for SVM; R2 = 0.993 and 0.975 for the ensemble model; p < 0.001). The magnitude of variation in AUC due to the reconstruction algorithm heterogeneity was 0.39 for SVM and 0.39 for the ensemble model. CONCLUSION: Inclusion of CT scans reconstructed with MBIR for radiomics modeling can significantly decrease diagnostic performance for the identification of IPAs.

Inspiratory Lung Expansion in Patients with Interstitial Lung Disease: CT Histogram Analyses.

This study aimed to evaluate inspiratory lung expansion in patients with interstitial lung disease (ILD) using histogram analyses based on advanced image registration between inspiratory and expiratory CT scans. We included 16 female ILD patients and eight age- and sex-matched normal controls who underwent full-inspiratory and expiratory CT scans. The CT scans were sequentially aligned based on the surface, landmarks, and attenuation of the lung parenchyma. Histogram analyses were performed on the degree of lung expansion (DLE) of each pixel between the aligned images in x-, y-, z-axes, and 3-dimensionally (3D). The overall mean registration error was 1.9 mm between the CT scans. The DLE3D in ILD patients was smaller than in the controls (mean, 17.6 mm vs. 26.9 mm; p = 0.023), and less heterogeneous in terms of standard deviation, entropy, and uniformity (p < 0.05). These results were mainly due to similar results in the DLEZ of the lower lungs. A forced vital capacity tended to be weakly correlated with mean (r2 = 0.210; p = 0.074), and histogram parameters (r2 = 0.194-0.251; p = 0.048-0.100) of the DLE3D in the lower lung in ILD patients. Our findings indicate that reduced and less heterogeneous inspiratory lung expansion in ILD patients can be identified by using advanced accurate image registration.

Improving the prediction of lung adenocarcinoma invasive component on CT: Value of a vessel removal algorithm during software segmentation of subsolid nodules.

PURPOSE: To evaluate the value of a vessel removal algorithm in segmentation of subsolid nodules by comparing the software solid component measurement on CT, before and after vessel removal, with the measurement of the invasive component on pathology in lung adenocarcinomas manifesting as subsolid nodules. MATERIALS AND METHODS: Between January 2014 and June 2015, 73 subsolid nodules with an invasive component of ≤10 mm on pathology were selected for analyses. For each nodule, semi-automated segmentation was performed by 2 radiologists and 3-dimensional (D) longest, axial longest and effective diameters of solid component were obtained from software, before and after using a vessel removal tool. These measurements were compared with the invasive component diameter on pathology using the paired t-test and Pearson's correlation test. RESULTS: Sixty-eight successfully segmented subsolid nodules were included. The mean maximal diameter of the invasive component on pathology was 4.6 mm (range, 0-10 mm). The correlation between software and pathology measurements was significant (p < 0.01) and the correlation after vessel removal (r = 0.49-0.54) was better than before vessel removal (r = 0.27-0.41). The mean measurement difference between solid component on CT and invasive tumor on pathology was significantly larger before vessel removal than after vessel removal in all measurements. The smallest mean measurement difference was obtained with 3D longest diameter of solid component after vessel removal in both readers (-0.26 mm to 0.10 mm), with no significant difference from pathology (p = 0.53-0.83). CONCLUSION: By adding a vessel removal algorithm in software segmentation of subsolid nodules, the prediction of invasive component in lung adenocarcinomas can be improved.

Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation.

PURPOSE: To develop an automatic deep feature classification (DFC) method for distinguishing benign angiomyolipoma without visible fat (AMLwvf) from malignant clear cell renal cell carcinoma (ccRCC) from abdominal contrast-enhanced computer tomography (CE CT) images. METHODS: A dataset including 80 abdominal CT images of 39 AMLwvf and 41 ccRCC patients was used. We proposed a DFC method for differentiating the small renal masses (SRM) into AMLwvf and ccRCC using the combination of hand-crafted and deep features, and machine learning classifiers. First, 71-dimensional hand-crafted features (HCF) of texture and shape were extracted from the SRM contours. Second, 1000-4000-dimensional deep features (DF) were extracted from the ImageNet pretrained deep learning model with the SRM image patches. In DF extraction, we proposed the texture image patches (TIP) to emphasize the texture information inside the mass in DFs and reduce the mass size variability. Finally, the two features were concatenated and the random forest (RF) classifier was trained on these concatenated features to classify the types of SRMs. The proposed method was tested on our dataset using leave-one-out cross-validation and evaluated using accuracy, sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and area under receiver operating characteristics curve (AUC). In experiments, the combinations of four deep learning models, AlexNet, VGGNet, GoogleNet, and ResNet, and four input image patches, including original, masked, mass-size, and texture image patches, were compared and analyzed. RESULTS: In qualitative evaluation, we observed the change in feature distributions between the proposed and comparative methods using tSNE method. In quantitative evaluation, we evaluated and compared the classification results, and observed that (a) the proposed HCF + DF outperformed HCF-only and DF-only, (b) AlexNet showed generally the best performances among the CNN models, and (c) the proposed TIPs not only achieved the competitive performances among the input patches, but also steady performance regardless of CNN models. As a result, the proposed method achieved the accuracy of 76.6 ± 1.4% for the proposed HCF + DF with AlexNet and TIPs, which improved the accuracy by 6.6%p and 8.3%p compared to HCF-only and DF-only, respectively. CONCLUSIONS: The proposed shape features and TIPs improved the HCFs and DFs, respectively, and the feature concatenation further enhanced the quality of features for differentiating AMLwvf from ccRCC in abdominal CE CT images.

Ground-glass nodule segmentation in chest CT images using asymmetric multi-phase deformable model and pulmonary vessel removal.

We propose a ground-glass nodule (GGN) segmentation method that can separate solid component and ground-glass opacity (GGO) using an asymmetric multi-phase deformable model in chest CT images. First, initial solid component and GGO were extracted using intensity-based segmentation with histogram modeling. Second, the initial extracted regions were refined using an asymmetric multi-phase deformable model with modified energy functional and intensity-constrained averaging function. Finally, vessel-like structures are removed based on multi-scale shape analysis. In experiments, the segmentation accuracy of the entire GGN was evaluated using datasets from SNUH and LIDC/IDRI. The average DSC values of Seoul National University Hospital (SNUH) and Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) were 0.85 ± 0.05 and 0.78 ± 0.07, respectively. The Pearson's correlation coefficient (r) between segmented volumes by the proposed method and manual segmentation was evaluated using SNUH dataset. The r of solid component, GGO, and entire GGN were 0.931, 0.875 and 0.907. Our experimental results show that the proposed method improves segmentation accuracy by applying the proposed asymmetric multiphase deformable model and pulmonary vessel removal.

The Bow Tie Shape of the Anterior Cruciate Ligament as Visualized by High-Resolution Magnetic Resonance Imaging.

BACKGROUND: Although numerous studies have examined the anatomic characteristics of the anterior cruciate ligament (ACL), its actual shape remains unclear. PURPOSE: To determine the average shape of the ACL by analyzing its cross section through the use of high-resolution magnetic resonance imaging (MRI) data. STUDY DESIGN: Descriptive laboratory study. METHODS: The study included 96 MRIs, conducted using a 3.0-T magnet, to analyze the shape of the ACL. Three-dimensional, curved multiplanar reconstruction was used to obtain cross sections at 7 points (femoral insertion; midsubstance 1, 2, 3, 4, and 5 from the femoral side to the tibial side; and tibial insertion). The width and thickness of cross sections were measured by 2 independent observers, and the ratio of width to thickness was calculated to determine the proportions of each cross section. The 7 cross sections were accumulated and standardized to generate an average model through the use of image analysis software developed by the authors. RESULTS: The mean ± SD width (femoral insertion, 17.02 ± 2.17 mm; tibial insertion, 17.33 ± 2.03 mm) and thickness (femoral insertion, 11.03 ± 1.75 mm; tibial insertion, 10.09 ± 1.70 mm) of both insertions were significantly larger than those of midsubstance 4 (width, 9.99 ± 1.87 mm; thickness, 6.53 ± 1.25 mm) ( P < .001). The mean ratios of width to thickness of the 7 cross sections from femoral insertion to tibial insertion were 1.57 ± 0.23, 3.36 ± 0.57, 3.07 ± 0.81, 2.18 ± 0.54, 1.56 ± 0.32, 2.16 ± 0.48, and 1.75 ± 0.28, respectively. The shape of the cross section at midsubstance 4 was an oval isthmus, which was the most narrow and well-balanced shape. It was transformed into a wide band at midsubstance 1 and 5. The shape of the femoral insertion was semicircular, with its anterior side slightly straight and its posterior side convex. The tibial insertion was kidney bean-shaped. CONCLUSION: On 3.0-T MRI, the ACL has a "bow tie" shape, including an oval isthmus, with a semicircular femoral insertion and kidney bean-shaped tibial insertion. CLINICAL RELEVANCE: The measurement method will allow surgeons to quantitatively diagnose partial injuries of the ACL using a noninvasive system in actual patients.