| 1. |
- Zhuang, Xiahai, et al.
(författare)
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Evaluation of algorithms for Multi-Modality Whole Heart Segmentation : An open-access grand challenge.
- 2019
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Ingår i: Medical Image Analysis. - : Elsevier BV. - 1361-8415 .- 1361-8423. ; 58
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Tidskriftsartikel (refereegranskat)abstract
- Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be challenging due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, an initial set of training data is generally needed for constructing priors or for training. Furthermore, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provided 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results showed that the performance of CT WHS was generally better than that of MRI WHS. The segmentation of the substructures for different categories of patients could present different levels of challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methods demonstrated great potential, though several of them reported poor results in the blinded evaluation. Their performance could vary greatly across different network structures and training strategies. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated good performance, though the accuracy and computational efficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/).
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| 2. |
- Astaraki, Mehdi, PhD Student, 1984-, et al.
(författare)
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A Comparative Study of Radiomics and Deep-Learning Based Methods for Pulmonary Nodule Malignancy Prediction in Low Dose CT Images
- 2021
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Ingår i: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: Both radiomics and deep learning methods have shown great promise in predicting lesion malignancy in various image-based oncology studies. However, it is still unclear which method to choose for a specific clinical problem given the access to the same amount of training data. In this study, we try to compare the performance of a series of carefully selected conventional radiomics methods, end-to-end deep learning models, and deep-feature based radiomics pipelines for pulmonary nodule malignancy prediction on an open database that consists of 1297 manually delineated lung nodules.Methods: Conventional radiomics analysis was conducted by extracting standard handcrafted features from target nodule images. Several end-to-end deep classifier networks, including VGG, ResNet, DenseNet, and EfficientNet were employed to identify lung nodule malignancy as well. In addition to the baseline implementations, we also investigated the importance of feature selection and class balancing, as well as separating the features learned in the nodule target region and the background/context region. By pooling the radiomics and deep features together in a hybrid feature set, we investigated the compatibility of these two sets with respect to malignancy prediction.Results: The best baseline conventional radiomics model, deep learning model, and deep-feature based radiomics model achieved AUROC values (mean ± standard deviations) of 0.792 ± 0.025, 0.801 ± 0.018, and 0.817 ± 0.032, respectively through 5-fold cross-validation analyses. However, after trying out several optimization techniques, such as feature selection and data balancing, as well as adding context features, the corresponding best radiomics, end-to-end deep learning, and deep-feature based models achieved AUROC values of 0.921 ± 0.010, 0.824 ± 0.021, and 0.936 ± 0.011, respectively. We achieved the best prediction accuracy from the hybrid feature set (AUROC: 0.938 ± 0.010).Conclusion: The end-to-end deep-learning model outperforms conventional radiomics out of the box without much fine-tuning. On the other hand, fine-tuning the models lead to significant improvements in the prediction performance where the conventional and deep-feature based radiomics models achieved comparable results. The hybrid radiomics method seems to be the most promising model for lung nodule malignancy prediction in this comparative study.
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| 3. |
- Li, Quan, et al.
(författare)
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Diagnostic performance of CT-derived resting distal to aortic pressure ratio (resting Pd/Pa) vs. CT-derived fractional flow reserve (CT-FFR) in coronary lesion severity assessment
- 2021
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Ingår i: Annals of Translational Medicine. - : AME Publishing Company. - 2305-5839 .- 2305-5847. ; 9:17
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Tidskriftsartikel (refereegranskat)abstract
- Background: Computed tomography-derived fractional flow reserve (CT-FFR) has emerged as a promising non-invasive substitute for fractional flow reserve (FFR) measurement. Normally, CT-FFR providing functional significance of coronary artery disease (CAD) by using a simplified total coronary resistance index (TCRI) model. Yet the error or discrepancy caused by this simplified model remains unclear. Methods: A total of 20 consecutive patients with suspected CAD who underwent CTA and invasive FFR measurement were retrospectively analyzed. CT-FFR and CT-(Pd/Pa)rest values derived from the coronary CTA images. The diagnostic performance of CT-FFR and CT-(Pd/Pa)rest were evaluated on a per-vessel level using C statistics with invasive FFR<0.80 as the reference standard. Results: Of the 25 vessels eventually analyzed, the prevalence of functionally significant CAD were 64%. The Youden index of the ROC curve indicated that the best cutoff value of invasive resting Pd/Pa was 0.945 for identifying functionally significant lesions. Sensitivity, specificity, negative predictive value, positive predictive value and accuracy were 85%, 91%, 92%, 83% and 88% for CT-(Pd/Pa)rest and 85%, 58% 69%, 78% and 72% for CT-FFR. Area under the receiver-operating characteristic curve (AUC) to detect functionally significant stenoses of CT-(Pd/Pa)rest and CT-FFR were 0.87 and 0.90. Conclusions: In this study, the results suggest CT-derived resting Pd/Pa has a potential advantage over CT-FFR in triaging patients for revascularization.
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| 4. |
- ODonnell, Michael, et al.
(författare)
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Registered Replication Report: Dijksterhuis and van Knippenberg (1998)
- 2018
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Ingår i: Perspectives on Psychological Science. - : SAGE PUBLICATIONS LTD. - 1745-6916 .- 1745-6924. ; 13:2, s. 268-294
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Tidskriftsartikel (refereegranskat)abstract
- Dijksterhuis and van Knippenberg (1998) reported that participants primed with a category associated with intelligence (professor) subsequently performed 13% better on a trivia test than participants primed with a category associated with a lack of intelligence (soccer hooligans). In two unpublished replications of this study designed to verify the appropriate testing procedures, Dijksterhuis, van Knippenberg, and Holland observed a smaller difference between conditions (2%-3%) as well as a gender difference: Men showed the effect (9.3% and 7.6%), but women did not (0.3% and -0.3%). The procedure used in those replications served as the basis for this multilab Registered Replication Report. A total of 40 laboratories collected data for this project, and 23 of these laboratories met all inclusion criteria. Here we report the meta-analytic results for those 23 direct replications (total N = 4,493), which tested whether performance on a 30-item general-knowledge trivia task differed between these two priming conditions (results of supplementary analyses of the data from all 40 labs, N = 6,454, are also reported). We observed no overall difference in trivia performance between participants primed with the professor category and those primed with the hooligan category (0.14%) and no moderation by gender.
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