Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network

BACKGROUND

In the management of cancer patients, determination of TNM status is essential for treatment decision-making and therefore closely linked to clinical outcome and survival. Here, we developed a tool for automatic three-dimensional (3D) localization and segmentation of cervical lymph nodes (LNs) on contrast-enhanced computed tomography (CECT) examinations.

METHODS

In this IRB-approved retrospective single-center study, 187 CECT examinations of the head and neck region from patients with various primary diseases were collected from our local database, and 3656 LNs (19.5 ± 14.9 LNs/CECT, mean ± standard deviation) with a short-axis diameter (SAD) ≥ 5 mm were segmented manually by expert physicians. With these data, we trained an independent fully convolutional neural network based on 3D foveal patches. Testing was performed on 30 independent CECTs with 925 segmented LNs with an SAD ≥ 5 mm.

RESULTS

In total, 4,581 LNs were segmented in 217 CECTs. The model achieved an average localization rate (LR), i.e., percentage of localized LNs/CECT, of 78.0% in the validation dataset. In the test dataset, average LR was 81.1% with a mean Dice coefficient of 0.71. For enlarged LNs with a SAD ≥ 10 mm, LR was 96.2%. In the test dataset, the false-positive rate was 2.4 LNs/CECT.

CONCLUSIONS

Our trained AI model demonstrated a good overall performance in the consistent automatic localization and 3D segmentation of physiological and metastatic cervical LNs with a SAD ≥ 5 mm on CECTs. This could aid clinical localization and automatic 3D segmentation, which can benefit clinical care and radiomics research.

RELEVANCE STATEMENT

Our AI model is a time-saving tool for 3D segmentation of cervical lymph nodes on contrast-enhanced CT scans and serves as a solid base for N staging in clinical practice and further radiomics research.

KEY POINTS

• Determination of N status in TNM staging is essential for therapy planning in oncology. • Segmenting cervical lymph nodes manually is highly time-consuming in clinical practice. • Our model provides a robust, automated 3D segmentation of cervical lymph nodes. • It achieves a high accuracy for localization especially of enlarged lymph nodes. • These segmentations should assist clinical care and radiomics research.

Responses of maize hybrids to water stress conditions at different developmental stages: accumulation of reactive oxygen species, activity of enzymatic antioxidants and degradation in kernel quality traits

Sustainable maize production under changing climatic conditions, especially heat and water stress conditions is one of the key challenges that need to be addressed immediately. The current field study was designed to evaluate the impact of water stress on morpho-physiological, biochemical, reactive oxygen species, antioxidant activity and kernel quality traits at different plant growth stages in maize hybrids. Four indigenous i.e., YH-5427, YH-5482, YH-5395, JPL-1908, and one multinational maize hybrid i.e., NK-8441 (Syngenta Seeds) were used for the study. Four stress treatments (i) Control (ii) 3-week water stress at pre-flowering stage (iii) 3-week water stress at anthesis stage (iv) 3-week water stress at grain filling/post-anthesis stage. The presence of significant oxidative stress was revealed by the overproduction of reactive oxygen species (ROXs) i.e., H2O2 (1.9 to 5.8 µmole g−1 FW) and malondialdehyde (120.5 to 169.0 nmole g−1 FW) leading to severe negative impacts on kernel yield. Moreover, a severe reduction in photosynthetic ability (50.6%, from 34.0 to 16.8 µmole m−2 s−1), lower transpirational rate (31.3%, from 3.2 to 2.2 mmol m−2 s−1), alterations in plant anatomy, reduced pigments stability, and deterioration of kernel quality was attributed to water stress. Water stress affected all the three studied growth stages, the pre-flowering stage being the most vulnerable while the post-anthesis stage was the least affected stage to drought stress. Antioxidant activity was observed to increase under all stress conditions in all maize hybrids, however, the highest antioxidant activity was recorded at the anthesis stage and in maize hybrids YH-5427 i.e., T-SOD activity was increased by 61.3% from 37.5 U mg−1 pro to 60.5 U mg−1 pro while CAT activity was maximum under water stress conditions 8.3 U mg−1 pro as compared to 10.3 U mg−1 pro under control (19.3%). The overall performance of maize hybrid YH-5427 was much more promising than other hybrids, attributed to its higher photosynthetic activity, and better antioxidant defense mechanism. Therefore, this hybrid could be recommended for cultivation in drought-prone areas.

Morphological appearance of the B.1.1.7 mutation of the novel coronavirus 2 (SARS-CoV-2) in chest CT

Background

Diagnosing a coronavirus disease 2019 (COVID-19) infection with high specificity in chest computed tomography (CT) imaging is considered possible due to distinctive imaging features of COVID-19 pneumonia. Since other viral non-COVID pneumonia show mostly a different distribution pattern, it is reasonable to assume that the patterns observed caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are a consequence of its genetically encoded molecular properties when interacting with the respiratory tissue. As more mutations of the initial SARS-CoV-2 wild-type with varying aggressiveness have been detected in the course of 2021, it became obvious that its genome is in a state of transformation and therefore a potential modification of the specific morphological appearance in CT may occur. The aim of this study was to quantitatively analyze the morphological differences of the SARS-CoV-2-B.1.1.7 mutation and wildtype variant in CT scans of the thorax.

Methods

We analyzed a dataset of 140 patients, which was divided into pneumonias caused by n=40 wildtype variants, n=40 B.1.1.7 variants, n=20 bacterial pneumonias, n=20 viral (non-COVID) pneumonias, and a test group of n=20 unremarkable CT examinations of the thorax. Semiautomated 3D segmentation of the lung tissue was performed for quantification of lung pathologies. The extent, ratio, and specific distribution of inflammatory affected lung tissue in each group were compared in a multivariate group analysis.

Results

Lung segmentation revealed significant difference between the extent of ground glass opacities (GGO) or consolidation comparing SARS-CoV-2 wild-type and B.1.1.7 variant. Wildtype and B.1.1.7 variant showed both a symmetric distribution pattern of stage-dependent GGO and consolidation within matched COVID-19 stages. Viral non-COVID pneumonias had significantly fewer consolidations than the bacterial, but also than the COVID-19 B.1.1.7 variant groups.

Conclusions

CT based segmentation showed no significant difference between the morphological appearance of the COVID-19 wild-type variant and the SARS-CoV-2 B.1.1.7 mutation. However, our approach allowed a semiautomatic quantification of bacterial and viral lung pathologies. Quantitative CT image analyses, such as the one presented, appear to be an important component of pandemic preparedness considering an organism with ongoing genetic change, to describe a potential arising change in CT morphological appearance of possible new upcoming COVID-19 variants of concern.

AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study

OBJECTIVES

Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence.

METHODS

We included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and German test dataset of n = 600 CT scans from six centers (COVID-19 / CAP; n = 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans; n = 90 each) was used to evaluate the radiologists' performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence.

RESULTS

The diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists' diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores.

CONCLUSION

This large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence.

KEY POINTS

• AI can help radiologists to get higher diagnostic accuracy, make faster decisions, and improve diagnostic confidence. • The China-German multicenter study demonstrates the advantages of a human-machine interaction using AI in clinical radiology for diagnostic differentiation between COVID-19 and CAP in CT scans.

Assessment of COVID-19 lung involvement on computed tomography by deep-learning-, threshold-, and human reader-based approaches—an international, multi-center comparative study

Background

The extent of lung involvement in coronavirus disease 2019 (COVID-19) pneumonia, quantified on computed tomography (CT), is an established biomarker for prognosis and guides clinical decision-making. The clinical standard is semi-quantitative scoring of lung involvement by an experienced reader. We aim to compare the performance of automated deep-learning- and threshold-based methods to the manual semi-quantitative lung scoring. Further, we aim to investigate an optimal threshold for quantification of involved lung in COVID pneumonia chest CT, using a multi-center dataset.

Methods

In total 250 patients were included, 50 consecutive patients with RT-PCR confirmed COVID-19 from our local institutional database, and another 200 patients from four international datasets (n=50 each). Lung involvement was scored semi-quantitatively by three experienced radiologists according to the established chest CT score (CCS) ranging from 0–25. Inter-rater reliability was reported by the intraclass correlation coefficient (ICC). Deep-learning-based segmentation of ground-glass and consolidation was obtained by CT Pulmo Auto Results prototype plugin on IntelliSpace Discovery (Philips Healthcare, The Netherlands). Threshold-based segmentation of involved lung was implemented using an open-source tool for whole-lung segmentation under the presence of severe pathologies (R231CovidWeb, Hofmanninger et al., 2020) and consecutive quantitative assessment of lung attenuation. The best threshold was investigated by training and testing a linear regression of deep-learning and threshold-based results in a five-fold cross validation strategy.

Results

Median CCS among 250 evaluated patients was 10 [6–15]. Inter-rater reliability of the CCS was excellent [ICC 0.97 (0.97–0.98)]. Best attenuation threshold for identification of involved lung was −522 HU. While the relationship of deep-learning- and threshold-based quantification was linear and strong (r²_{deep-learningvs.threshold}=0.84), both automated quantification methods translated to the semi-quantitative CCS in a non-linear fashion, with an increasing slope towards higher CCS (r²_{deep-learningvs.CCS}= 0.80, r²_{thresholdvs.CCS}=0.63).

Conclusions

The manual semi-quantitative CCS underestimates the extent of COVID pneumonia in higher score ranges, which limits its clinical usefulness in cases of severe disease. Clinical implementation of fully automated methods, such as deep-learning or threshold-based approaches (best threshold in our multi-center dataset: −522 HU), might save time of trained personnel, abolish inter-reader variability, and allow for truly quantitative, linear assessment of COVID lung involvement.

Spectral Detector CT-Derived Pulmonary Perfusion Maps and Pulmonary Parenchyma Characteristics for the Semiautomated Classification of Pulmonary Hypertension

Objectives

To evaluate the usefulness of spectral detector CT (SDCT)-derived pulmonary perfusion maps and pulmonary parenchyma characteristics for the semiautomated classification of pulmonary hypertension (PH).

Methods

A total of 162 consecutive patients with right heart catheter (RHC)-proven PH of different aetiologies as defined by the current ESC/ERS guidelines who underwent CT pulmonary angiography (CTPA) on SDCT and 20 patients with an invasive rule-out of PH were included in this retrospective study. Semiautomatic lung segmentation into normal and malperfused areas based on iodine density (ID) as well as automatic, virtual non-contrast-based emphysema quantification were performed. Corresponding volumes, histogram features and the ID SkewnessPerfDef-Emphysema-Index (δ-index) accounting for the ratio of ID distribution in malperfused lung areas and the proportion of emphysematous lung parenchyma were computed and compared between groups.

Results

Patients with PH showed a significantly greater extent of malperfused lung areas as well as stronger and more homogenous perfusion defects. In group 3 and 4 patients, ID skewness revealed a significantly more homogenous ID distribution in perfusion defects than in all other subgroups. The δ-index allowed for further subclassification of subgroups 3 and 4 (p < 0.001), identifying patients with chronic thromboembolic PH (CTEPH, subgroup 4) with high accuracy (AUC: 0.92, 95%-CI, 0.85–0.99).

Conclusion

Abnormal pulmonary perfusion in PH can be detected and quantified by semiautomated SDCT-based pulmonary perfusion maps. ID skewness in malperfused lung areas, and the δ-index allow for a classification of PH subgroups, identifying groups 3 and 4 patients with high accuracy, independent of reader expertise.

Biofortification of Cereals and Pulses Using New Breeding Techniques: Current and Future Perspectives

Cereals and pulses are consumed as a staple food in low-income countries for the fulfillment of daily dietary requirements and as a source of micronutrients. However, they are failing to offer balanced nutrition due to deficiencies of some essential compounds, macronutrients, and micronutrients, i.e., cereals are deficient in iron, zinc, some essential amino acids, and quality proteins. Meanwhile, the pulses are rich in anti-nutrient compounds that restrict the bioavailability of micronutrients. As a result, the population is suffering from malnutrition and resultantly different diseases, i.e., anemia, beriberi, pellagra, night blindness, rickets, and scurvy are common in the society. These facts highlight the need for the biofortification of cereals and pulses for the provision of balanced diets to masses and reduction of malnutrition. Biofortification of crops may be achieved through conventional approaches or new breeding techniques (NBTs). Conventional approaches for biofortification cover mineral fertilization through foliar or soil application, microbe-mediated enhanced uptake of nutrients, and conventional crossing of plants to obtain the desired combination of genes for balanced nutrient uptake and bioavailability. Whereas, NBTs rely on gene silencing, gene editing, overexpression, and gene transfer from other species for the acquisition of balanced nutritional profiles in mutant plants. Thus, we have highlighted the significance of conventional and NBTs for the biofortification of cereals and pulses. Current and future perspectives and opportunities are also discussed. Further, the regulatory aspects of newly developed biofortified transgenic and/or non-transgenic crop varieties via NBTs are also presented.

CRISPR-Based Crop Improvements: A Way Forward to Achieve Zero Hunger

Zero hunger is one of the sustainable development goals set by the United Nations in 2015 to achieve global food security by 2030. The current harvest of crops is insufficient; feeding the world's population and meeting the goal of zero hunger by 2030 will require larger and more consistent crop production. Clustered regularly interspaced short palindromic repeats-associated protein (CRISPR-Cas) technology is widely used for the plant genome editing. In this review, we consider this technology as a potential tool for achieving zero hunger. We provide a comprehensive overview of CRISPR-Cas technology and its most important applications for food crops' improvement. We also conferred current and potential technological breakthroughs that will help in breeding future crops to end global hunger. The regulatory aspects of deploying this technology in commercial sectors, bioethics, and the production of transgene-free plants are also discussed. We hope that the CRISPR-Cas system will accelerate the breeding of improved crop cultivars compared with conventional breeding and pave the way toward the zero hunger goal.

Advanced Deep Learning Approach to Automatically Segment Malignant Tumors and Ablation Zone in the Liver With Contrast-Enhanced CT

OBJECTIVE

Liver cancer is one of the most commonly diagnosed cancer, and energy-based tumor ablation is a widely accepted treatment. Automatic and robust segmentation of liver tumors and ablation zones would facilitate the evaluation of treatment success. The purpose of this study was to develop and evaluate an automatic deep learning based method for (1) segmentation of liver and liver tumors in both arterial and portal venous phase for pre-treatment CT, and (2) segmentation of liver and ablation zones in both arterial and portal venous phase for after ablation treatment.

MATERIALS AND METHODS

252 CT images from 63 patients undergoing liver tumor ablation at a large University Hospital were retrospectively included; each patient had pre-treatment and post-treatment multi-phase CT images. 3D voxel-wise manual segmentation of the liver, tumors and ablation region by the radiologist provided reference standard. Deep learning models for liver and lesion segmentation were initially trained on the public Liver Tumor Segmentation Challenge (LiTS) dataset to obtain base models. Then, transfer learning was applied to adapt the base models on the clinical training-set, to obtain tumor and ablation segmentation models both for arterial and portal venous phase images. For modeling, 2D residual-attention Unet (RA-Unet) was employed for liver segmentation and a multi-scale patch-based 3D RA-Unet for tumor and ablation segmentation.

RESULTS

On the independent test-set, the proposed method achieved a dice similarity coefficient (DSC) of 0.96 and 0.95 for liver segmentation on arterial and portal venous phase, respectively. For liver tumors, the model on arterial phase achieved detection sensitivity of 71%, DSC of 0.64, and on portal venous phase sensitivity of 82%, DSC of 0.73. For liver tumors >0.5cm3 performance improved to sensitivity 79%, DSC 0.65 on arterial phase and, sensitivity 86%, DSC 0.72 on portal venous phase. For ablation zone, the model on arterial phase achieved detection sensitivity of 90%, DSC of 0.83, and on portal venous phase sensitivity of 90%, DSC of 0.89.

CONCLUSION

The proposed deep learning approach can provide automated segmentation of liver tumors and ablation zones on multi-phase (arterial and portal venous) and multi-time-point (before and after treatment) CT enabling quantitative evaluation of treatment success.

Fully Automated MR Detection and Segmentation of Brain Metastases in Non-small Cell Lung Cancer Using Deep Learning

BACKGROUND

Non-small cell lung cancer (NSCLC) is the most common tumor entity spreading to the brain and up to 50% of patients develop brain metastases (BMs). Detection of BMs on MRI is challenging with an inherent risk of missed diagnosis.

PURPOSE

To train and evaluate a deep learning model (DLM) for fully automated detection and 3D segmentation of BMs in NSCLC on clinical routine MRI.

STUDY TYPE

Retrospective.

POPULATION

Ninety-eight NSCLC patients with 315 BMs on pretreatment MRI, divided into training (66 patients, 248 BMs) and independent test (17 patients, 67 BMs) and control (15 patients, 0 BMs) cohorts.

FIELD STRENGTH/SEQUENCE

T₁ -/T₂ -weighted, T₁ -weighted contrast-enhanced (T₁ CE; gradient-echo and spin-echo sequences), and FLAIR at 1.0, 1.5, and 3.0 T from various vendors and study centers.

ASSESSMENT

A 3D convolutional neural network (DeepMedic) was trained on the training cohort using 5-fold cross-validation and evaluated on the independent test and control sets. Three-dimensional voxel-wise manual segmentations of BMs by a neurosurgeon and a radiologist on T₁ CE served as the reference standard.

STATISTICAL TESTS

Sensitivity (recall) and false positive (FP) findings per scan, dice similarity coefficient (DSC) to compare the spatial overlap between manual and automated segmentations, Pearson's correlation coefficient (r) to evaluate the relationship between quantitative volumetric measurements of segmentations, and Wilcoxon rank-sum test to compare the volumes of BMs. A P value <0.05 was considered statistically significant.

RESULTS

In the test set, the DLM detected 57 of the 67 BMs (mean volume: 0.99 ± 4.24 cm³ ), resulting in a sensitivity of 85.1%, while FP findings of 1.5 per scan were observed. Missed BMs had a significantly smaller volume (0.05 ± 0.04 cm³ ) than detected BMs (0.96 ± 2.4 cm³ ). Compared with the reference standard, automated segmentations achieved a median DSC of 0.72 and a good volumetric correlation (r = 0.95). In the control set, 1.8 FPs/scan were observed.

DATA CONCLUSION

Deep learning provided a high detection sensitivity and good segmentation performance for BMs in NSCLC on heterogeneous scanner data while yielding a low number of FP findings. Level of Evidence 3 Technical Efficacy Stage 2.

Deep learning assistance increases the detection sensitivity of radiologists for secondary intracranial aneurysms in subarachnoid hemorrhage

Purpose

To evaluate whether a deep learning model (DLM) could increase the detection sensitivity of radiologists for intracranial aneurysms on CT angiography (CTA) in aneurysmal subarachnoid hemorrhage (aSAH).

Methods

Three different DLMs were trained on CTA datasets of 68 aSAH patients with 79 aneurysms with their outputs being combined applying ensemble learning (DLM-Ens). The DLM-Ens was evaluated on an independent test set of 104 aSAH patients with 126 aneuryms (mean volume 129.2 ± 185.4 mm³, 13.0% at the posterior circulation), which were determined by two radiologists and one neurosurgeon in consensus using CTA and digital subtraction angiography scans. CTA scans of the test set were then presented to three blinded radiologists (reader 1: 13, reader 2: 4, and reader 3: 3 years of experience in diagnostic neuroradiology), who assessed them individually for aneurysms. Detection sensitivities for aneurysms of the readers with and without the assistance of the DLM were compared.

Results

In the test set, the detection sensitivity of the DLM-Ens (85.7%) was comparable to the radiologists (reader 1: 91.2%, reader 2: 86.5%, and reader 3: 86.5%; Fleiss κ of 0.502). DLM-assistance significantly increased the detection sensitivity (reader 1: 97.6%, reader 2: 97.6%,and reader 3: 96.0%; overall P=.024; Fleiss κ of 0.878), especially for secondary aneurysms (88.2% of the additional aneurysms provided by the DLM).

Conclusion

Deep learning significantly improved the detection sensitivity of radiologists for aneurysms in aSAH, especially for secondary aneurysms. It therefore represents a valuable adjunct for physicians to establish an accurate diagnosis in order to optimize patient treatment.

Harnessing the potential of plant transcription factors in developing climate resilient crops to improve global food security: Current and future perspectives

Crop plants should be resilient to climatic factors in order to feed ever-increasing populations. Plants have developed stress-responsive mechanisms by changing their metabolic pathways and switching the stress-responsive genes. The discovery of plant transcriptional factors (TFs), as key regulators of different biotic and abiotic stresses, has opened up new horizons for plant scientists. TFs perceive the signal and switch certain stress-responsive genes on and off by binding to different cis-regulatory elements. More than 50 families of plant TFs have been reported in nature. Among them, DREB, bZIP, MYB, NAC, Zinc-finger, HSF, Dof, WRKY, and NF-Y are important with respect to biotic and abiotic stresses, but the potential of many TFs in the improvement of crops is untapped. In this review, we summarize the role of different stress-responsive TFs with respect to biotic and abiotic stresses. Further, challenges and future opportunities linked with TFs for developing climate-resilient crops are also elaborated.

Automated Detection and Segmentation of Brain Metastases in Malignant Melanoma: Evaluation of a Dedicated Deep Learning Model

BACKGROUND AND PURPOSE

Malignant melanoma is an aggressive skin cancer in which brain metastases are common. Our aim was to establish and evaluate a deep learning model for fully automated detection and segmentation of brain metastases in patients with malignant melanoma using clinical routine MR imaging.

MATERIALS AND METHODS

Sixty-nine patients with melanoma with a total of 135 brain metastases at initial diagnosis and available multiparametric MR imaging datasets (T1-/T2-weighted, T1-weighted gadolinium contrast-enhanced, FLAIR) were included. A previously established deep learning model architecture (3D convolutional neural network; DeepMedic) simultaneously operating on the aforementioned MR images was trained on a cohort of 55 patients with 103 metastases using 5-fold cross-validation. The efficacy of the deep learning model was evaluated using an independent test set consisting of 14 patients with 32 metastases. Manual segmentations of metastases in a voxelwise manner (T1-weighted gadolinium contrast-enhanced imaging) performed by 2 radiologists in consensus served as the ground truth.

RESULTS

After training, the deep learning model detected 28 of 32 brain metastases (mean volume, 1.0 [SD, 2.4] cm³) in the test cohort correctly (sensitivity of 88%), while false-positive findings of 0.71 per scan were observed. Compared with the ground truth, automated segmentations achieved a median Dice similarity coefficient of 0.75.

CONCLUSIONS

Deep learning-based automated detection and segmentation of brain metastases in malignant melanoma yields high detection and segmentation accuracy with false-positive findings of <1 per scan.

Role of Genetics, Genomics, and Breeding Approaches to Combat Stripe Rust of Wheat

Puccinia striiformis (Pst) is a devastating biotrophic fungal pathogen that causes wheat stripe rust. It usually loves cool and moist places and can cause 100% crop yield losses in a single field when ideal conditions for disease incidence prevails. Billions of dollars are lost due to fungicide application to reduce stripe rust damage worldwide. Pst is a macrocyclic, heteroecious fungus that requires primary (wheat or grasses) as well as secondary host (Berberis or Mahonia spp.) for completion of life cycle. In this review, we have summarized the knowledge about pathogen life cycle, genes responsible for stripe rust resistance, and susceptibility in wheat. In the end, we discussed the importance of conventional and modern breeding tools for the development of Pst-resistant wheat varieties. According to our findings, genetic engineering and genome editing are less explored tools for the development of Pst-resistant wheat varieties; hence, we highlighted the putative use of advanced genome-modifying tools, i.e., base editing and prime editing, for the development of Pst-resistant wheat.

The level of Cry1Ac endotoxin and its efficacy against H. armigera in Bt cotton at large scale in Pakistan

A biophysical survey was conducted in 15 cotton-growing districts of Pakistan. Four hundred cotton growers were approached and inquired about the production technology of Bt cotton. Further, 25 strip tests using combo strips (Cry1Ac, Cry2Ab, Vip3Aa and Cp4, EPSPS gene) were performed at each farmer’s field. Out of 10,000 total-tested samples, farmers claimed 9682 samples as Bt and 318 samples as non-Bt. After performing a strip test, 1009 and 87 samples were found false negative and false positive, respectively. Only 53 samples were found positive for Cry2Ab, 214 for EPSPS and none for Vip3Aa gene. Quantification of Cry endotoxin and bioassay studies were performed by taking leaves from upper, middle, and lower canopies, and fruiting parts at approximately 80 days after sowing from 89 varieties. Expression was highly variable among different canopies and fruiting parts. Moreover, Cry endotoxin expression and insect mortality varied significantly among varieties from 0.26 µg g⁻¹ to 3.54 µg g⁻¹ with mortality ranging from 28 to 97%, respectively. Highest Cry1Ac expression (3.54 µg g⁻¹) and insect mortality (97%) were observed for variety FH-142 from DG Khan. Cry endotoxin expression varied significantly across various plant parts, i.e., IUB-13 variety from upper canopy documented 0.34 µg g⁻¹ expression with 37% insect mortality in Layyah to 3.42 µg g⁻¹ expression and 96% insect mortality from DG Khan. Lethal dose, LD95 (2.20 µg g⁻¹) of Cry1Ac endotoxin was optimized for effective control of H. armigera. Our results provided evidence of practical resistance in H. armigera and way forward.

Neurological complications in patients with Plasmodium vivax malaria from Karachi, Pakistan

BACKGROUND

Malaria remains an endemic disease in Pakistan with an estimated healthcare burden of 1.6 million cases annually, with Plasmodium vivax accounting for 67% of reported cases. P. vivax is the most common species causing malaria outside of Africa, with approximately 13.8 million reported cases worldwide.

METHOD

We report a series of P. vivax cases with cerebral involvement that presented at Aga Khan University Hospital, Karachi, Pakistan.

RESULTS

The majority of the patients presented with high-grade fever accompanied by projectile vomiting and abnormal behaviour, seizures, shock and unconsciousness. Seven of 801 patients with P. vivax monoinfection presented or developed cerebral complications. P. vivax infections were diagnosed based on peripheral smears and rapid diagnostic testing.

CONCLUSION

P. vivax infection can lead to severe complications, although not with the frequency of Plasmodium falciparum infection. Current cases highlight an increasing trend of cerebral complications caused by P. vivax.

Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer

Purpose

To develop and validate a robust and accurate registration pipeline for automatic contour propagation for online adaptive Intensity‐Modulated Proton Therapy (IMPT) of prostate cancer using elastix software and deep learning.

Methods

A three‐dimensional (3D) Convolutional Neural Network was trained for automatic bladder segmentation of the computed tomography (CT) scans. The automatic bladder segmentation alongside the computed tomography (CT) scan is jointly optimized to add explicit knowledge about the underlying anatomy to the registration algorithm. We included three datasets from different institutes and CT manufacturers. The first was used for training and testing the ConvNet, where the second and the third were used for evaluation of the proposed pipeline. The system performance was quantified geometrically using the dice similarity coefficient (DSC), the mean surface distance (MSD), and the 95% Hausdorff distance (HD). The propagated contours were validated clinically through generating the associated IMPT plans and compare it with the IMPT plans based on the manual delineations. Propagated contours were considered clinically acceptable if their treatment plans met the dosimetric coverage constraints on the manual contours.

Results

The bladder segmentation network achieved a DSC of 88% and 82% on the test datasets. The proposed registration pipeline achieved a MSD of 1.29 ± 0.39, 1.48 ± 1.16, and 1.49 ± 0.44 mm for the prostate, seminal vesicles, and lymph nodes, respectively, on the second dataset and a MSD of 2.31 ± 1.92 and 1.76 ± 1.39 mm for the prostate and seminal vesicles on the third dataset. The automatically propagated contours met the dose coverage constraints in 86%, 91%, and 99% of the cases for the prostate, seminal vesicles, and lymph nodes, respectively. A Conservative Success Rate (CSR) of 80% was obtained, compared to 65% when only using intensity‐based registration.

Conclusion

The proposed registration pipeline obtained highly promising results for generating treatment plans adapted to the daily anatomy. With 80% of the automatically generated treatment plans directly usable without manual correction, a substantial improvement in system robustness was reached compared to a previous approach. The proposed method therefore facilitates more precise proton therapy of prostate cancer, potentially leading to fewer treatment‐related adverse side effects.

Association of cardiovascular magnetic resonance-derived circumferential strain parameters with the risk of ventricular arrhythmia and all-cause mortality in patients with prior myocardial infarction and primary prevention implantable cardioverter defibrillator

BACKGROUND

Impaired left ventricular (LV) contraction and relaxation may further promote adverse remodeling and may increase the risk of ventricular arrhythmia (VA) in ischemic cardiomyopathy. We aimed to examine the association of cardiovascular magnetic resonance (CMR)-derived circumferential strain parameters for LV regional systolic function, LV diastolic function and mechanical dispersion with the risk of VA in patients with prior myocardial infarction and primary prevention implantable cardioverter defibrillator (ICD).

METHODS

Patients with an ischemic cardiomyopathy who underwent CMR prior to primary prevention ICD implantation, were retrospectively identified. LV segmental circumferential strain curves were extracted from short-axis cine CMR. For LV regional strain analysis, the extent of moderately and severely impaired strain (percentage of LV segments with strain between - 10% and - 5% and > - 5%, respectively) were calculated. LV diastolic function was quantified by the early and late diastolic strain rate. Mechanical dispersion was defined as the standard deviation in delay time between each strain curve and the patient-specific reference curve. Cox proportional hazard ratios (HR) (95%CI) were calculated to assess the association between LV strain parameters and appropriate ICD therapy.

RESULTS

A total of 121 patients (63 ± 11 years, 84% men, LV ejection fraction (LVEF) 27 ± 9%) were included. During a median (interquartile range) follow-up of 47 (27;69) months, 30 (25%) patients received appropriate ICD therapy. The late diastolic strain rate (HR 1.1 (1.0;1.2) per - 0.25 1/s, P = 0.043) and the extent of moderately impaired strain (HR 1.5 (1.0;2.2) per + 10%, P = 0.048) but not the extent of severely impaired strain (HR 0.9 (0.6;1.4) per + 10%, P = 0.685) were associated with appropriate ICD therapy, independent of LVEF, late gadolinium enhancement (LGE) scar border size and acute revascularization. Mechanical dispersion was not related to appropriate ICD therapy (HR 1.1 (0.8;1.6) per + 25 ms, P = 0.464).

CONCLUSIONS

In an ischemic cardiomyopathy population referred for primary prevention ICD implantation, the extent of moderately impaired strain and late diastolic strain rate were associated with the risk of appropriate ICD therapy, independent of LVEF, scar border size and acute revascularization. These findings suggest that disturbed LV contraction and relaxation may contribute to an increased risk of VA after myocardial infarction.

Quantification of aortic pulse wave velocity from a population based cohort: a fully automatic method

BACKGROUND

Aortic pulse wave velocity (PWV) is an indicator of aortic stiffness and is used as a predictor of adverse cardiovascular events. PWV can be non-invasively assessed using magnetic resonance imaging (MRI). PWV computation requires two components, the length of the aortic arch and the time taken for the systolic pressure wave to travel through the aortic arch. The aortic length is calculated using a multi-slice 3D scan and the transit time is computed using a 2D velocity encoded MRI (VE) scan. In this study we present and evaluate an automatic method to quantify the aortic pulse wave velocity using a large population-based cohort.

METHODS

For this study 212 subjects were retrospectively selected from a large multi-center heart-brain connection cohort. For each subject a multi-slice 3D scan of the aorta was acquired in an oblique-sagittal plane and a 2D VE scan acquired in a transverse plane cutting through the proximal ascending and descending aorta. PWV was calculated in three stages: (i) a multi-atlas-based segmentation method was developed to segment the aortic arch from the multi-slice 3D scan and subsequently estimate the length of the proximal aorta, (ii) an algorithm that delineates the proximal ascending and descending aorta from the time-resolved 2D VE scan and subsequently obtains the velocity-time flow curves was also developed, and (iii) automatic methods that can compute the transit time from the velocity-time flow curves were implemented and investigated. Finally the PWV was obtained by combining the aortic length and the transit time.

RESULTS

Quantitative evaluation with respect to the length of the aortic arch as well as the computed PWV were performend by comparing the results of the novel automatic method to those obtained manually. The mean absolute difference in aortic length obtained automatically as compared to those obtained manually was 3.3 ± 2.8 mm (p < 0.05), the manual inter-observer variability on a subset of 45 scans was 3.4 ± 3.4 mm (p = 0.49). Bland-Altman analysis between the automataic method and the manual methods showed a bias of 0.0 (-5.0,5.0) m/s for the foot-to-foot approach, -0.1 (-1.2, 1.1) and -0.2 (-2.6, 2.1) m/s for the half-max and the cross-correlation methods, respectively.

CONCLUSION

We proposed and evaluated a fully automatic method to calculate the PWV on a large set of multi-center MRI scans. It was observed that the overall results obtained had very good agreement with manual analysis. Our proposed automatic method would be very beneficial for large population based studies, where manual analysis requires a lot of manpower.

Learning-based automated segmentation of the carotid artery vessel wall in dual-sequence MRI using subdivision surface fitting

PURPOSE

The quantification of vessel wall morphology and plaque burden requires vessel segmentation, which is generally performed by manual delineations. The purpose of our work is to develop and evaluate a new 3D model-based approach for carotid artery wall segmentation from dual-sequence MRI.

METHODS

The proposed method segments the lumen and outer wall surfaces including the bifurcation region by fitting a subdivision surface constructed hierarchical-tree model to the image data. In particular, a hybrid segmentation which combines deformable model fitting with boundary classification was applied to extract the lumen surface. The 3D model ensures the correct shape and topology of the carotid artery, while the boundary classification uses combined image information of 3D TOF-MRA and 3D BB-MRI to promote accurate delineation of the lumen boundaries. The proposed algorithm was validated on 25 subjects (48 arteries) including both healthy volunteers and atherosclerotic patients with 30% to 70% carotid stenosis.

RESULTS

For both lumen and outer wall border detection, our result shows good agreement between manually and automatically determined contours, with contour-to-contour distance less than 1 pixel as well as Dice overlap greater than 0.87 at all different carotid artery sections.

CONCLUSIONS

The presented 3D segmentation technique has demonstrated the capability of providing vessel wall delineation for 3D carotid MRI data with high accuracy and limited user interaction. This brings benefits to large-scale patient studies for assessing the effect of pharmacological treatment of atherosclerosis by reducing image analysis time and bias between human observers.

Design of the ExCersion-VCI study: The effect of aerobic exercise on cerebral perfusion in patients with vascular cognitive impairment

There is evidence for a beneficial effect of aerobic exercise on cognition, but underlying mechanisms are unclear. In this study, we test the hypothesis that aerobic exercise increases cerebral blood flow (CBF) in patients with vascular cognitive impairment (VCI). This study is a multicenter single-blind randomized controlled trial among 80 patients with VCI. Most important inclusion criteria are a diagnosis of VCI with Mini-Mental State Examination ≥22 and Clinical Dementia Rating ≤0.5. Participants are randomized into an aerobic exercise group or a control group. The aerobic exercise program aims to improve cardiorespiratory fitness and takes 14 weeks, with a frequency of three times a week. Participants are provided with a bicycle ergometer at home. The control group receives two information meetings. Primary outcome measure is change in CBF. We expect this study to provide insight into the potential mechanism by which aerobic exercise improves hemodynamic status.

Automatic segmentation and quantification of the cardiac structures from non-contrast-enhanced cardiac CT scans

Early structural changes to the heart, including the chambers and the coronary arteries, provide important information on pre-clinical heart disease like cardiac failure. Currently, contrast-enhanced cardiac computed tomography angiography (CCTA) is the preferred modality for the visualization of the cardiac chambers and the coronaries. In clinical practice not every patient undergoes a CCTA scan; many patients receive only a non-contrast-enhanced calcium scoring CT scan (CTCS), which has less radiation dose and does not require the administration of contrast agent. Quantifying cardiac structures in such images is challenging, as they lack the contrast present in CCTA scans. Such quantification would however be relevant, as it enables population based studies with only a CTCS scan. The purpose of this work is therefore to investigate the feasibility of automatic segmentation and quantification of cardiac structures viz whole heart, left atrium, left ventricle, right atrium, right ventricle and aortic root from CTCS scans. A fully automatic multi-atlas-based segmentation approach is used to segment the cardiac structures. Results show that the segmentation overlap between the automatic method and that of the reference standard have a Dice similarity coefficient of 0.91 on average for the cardiac chambers. The mean surface-to-surface distance error over all the cardiac structures is [Formula: see text] mm. The automatically obtained cardiac chamber volumes using the CTCS scans have an excellent correlation when compared to the volumes in corresponding CCTA scans, a Pearson correlation coefficient (R) of 0.95 is obtained. Our fully automatic method enables large-scale assessment of cardiac structures on non-contrast-enhanced CT scans.

An evaluation of automatic coronary artery calcium scoring methods with cardiac CT using the orCaScore framework

PURPOSE

The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular disease (CVD) events. In clinical practice, CAC is manually identified and automatically quantified in cardiac CT using commercially available software. This is a tedious and time-consuming process in large-scale studies. Therefore, a number of automatic methods that require no interaction and semiautomatic methods that require very limited interaction for the identification of CAC in cardiac CT have been proposed. Thus far, a comparison of their performance has been lacking. The objective of this study was to perform an independent evaluation of (semi)automatic methods for CAC scoring in cardiac CT using a publicly available standardized framework.

METHODS

Cardiac CT exams of 72 patients distributed over four CVD risk categories were provided for (semi)automatic CAC scoring. Each exam consisted of a noncontrast-enhanced calcium scoring CT (CSCT) and a corresponding coronary CT angiography (CCTA) scan. The exams were acquired in four different hospitals using state-of-the-art equipment from four major CT scanner vendors. The data were divided into 32 training exams and 40 test exams. A reference standard for CAC in CSCT was defined by consensus of two experts following a clinical protocol. The framework organizers evaluated the performance of (semi)automatic methods on test CSCT scans, per lesion, artery, and patient.

RESULTS

Five (semi)automatic methods were evaluated. Four methods used both CSCT and CCTA to identify CAC, and one method used only CSCT. The evaluated methods correctly detected between 52% and 94% of CAC lesions with positive predictive values between 65% and 96%. Lesions in distal coronary arteries were most commonly missed and aortic calcifications close to the coronary ostia were the most common false positive errors. The majority (between 88% and 98%) of correctly identified CAC lesions were assigned to the correct artery. Linearly weighted Cohen's kappa for patient CVD risk categorization by the evaluated methods ranged from 0.80 to 1.00.

CONCLUSIONS

A publicly available standardized framework for the evaluation of (semi)automatic methods for CAC identification in cardiac CT is described. An evaluation of five (semi)automatic methods within this framework shows that automatic per patient CVD risk categorization is feasible. CAC lesions at ambiguous locations such as the coronary ostia remain challenging, but their detection had limited impact on CVD risk determination.

Lumen segmentation and stenosis quantification of atherosclerotic carotid arteries in CTA utilizing a centerline intensity prior

PURPOSE

The degree of stenosis is an important biomarker in assessing the severity of cardiovascular disease. The purpose of our work is to develop and evaluate a semiautomatic method for carotid lumen segmentation and subsequent carotid artery stenosis quantification in CTA images.

METHODS

The authors present a semiautomatic stenosis detection and quantification method following lumen segmentation. The lumen of the carotid arteries is segmented in three steps. First, centerlines of the internal and external carotid arteries are extracted with an iterative minimum cost path approach in which the costs are based on a measure of medialness and intensity similarity to lumen. Second, the lumen boundary is delineated using a level set procedure which is steered by gradient information, regional intensity information, and spatial information. Special effort is made in adding terms based on local centerline intensity prior so as to exclude all possible plaque tissues from the segmentation. Third, side branches in the segmented lumen are removed by applying a shape constraint to the envelope of the maximum inscribed spheres of the segmentation. From the segmented lumen, the authors detect and quantify the cross-sectional area-based and cross-sectional diameter-based stenosis degrees according to the North American Symptomatic Carotid En-darterectomy Trial criterion.

RESULTS

The method is trained and tested on a publicly available database from the cls2009 challenge. For the segmentation, the authors obtain a Dice similarity coefficient of 90.2% and a mean absolute surface distance of 0.34 mm. For the stenosis quantification, the authors obtain an average error of 15.7% for cross-sectional diameter-based stenosis and 19.2% for cross-sectional area-based stenosis quantification.

CONCLUSIONS

With these results, the method ranks second in terms of carotid lumen segmentation accuracy, and first in terms of carotid artery stenosis quantification.

Late prosthetic valve endocarditis caused by Staphylococcus hemolyticus

We describe a case of late PVE in a 78-year-old man that was caused by Stapylococcus hemolyticus and occurred 5 years after aortic valve replacement. This is the first reported case of PVE due to this organism.