In Medical & biological engineering & computing ; h5-index 32.0

Rothstock Stephan, Weiss Hans-Rudolf, Krueger Daniel, Paul Lothar

2020-Oct-01

3D surface scan, Asymmetry distance map, Classification, Machine learning, Scoliosis

In European radiology ; h5-index 62.0

OBJECTIVES : To apply deep learning algorithms using a conventional convolutional neural network (CNN) and a recurrent CNN to differentiate three breast cancer molecular subtypes on MRI.

METHODS : A total of 244 patients were analyzed, 99 in training dataset scanned at 1.5 T and 83 in testing-1 and 62 in testing-2 scanned at 3 T. Patients were classified into 3 subtypes based on hormonal receptor (HR) and HER2 receptor: (HR+/HER2-), HER2+, and triple negative (TN). Only images acquired in the DCE sequence were used in the analysis. The smallest bounding box covering tumor ROI was used as the input for deep learning to develop the model in the training dataset, by using a conventional CNN and the convolutional long short-term memory (CLSTM). Then, transfer learning was applied to re-tune the model using testing-1(2) and evaluated in testing-2(1).

RESULTS : In the training dataset, the mean accuracy evaluated using tenfold cross-validation was higher by using CLSTM (0.91) than by using CNN (0.79). When the developed model was applied to the independent testing datasets, the accuracy was 0.4-0.5. With transfer learning by re-tuning parameters in testing-1, the mean accuracy reached 0.91 by CNN and 0.83 by CLSTM, and improved accuracy in testing-2 from 0.47 to 0.78 by CNN and from 0.39 to 0.74 by CLSTM. Overall, transfer learning could improve the classification accuracy by greater than 30%.

CONCLUSIONS : The recurrent network using CLSTM could track changes in signal intensity during DCE acquisition, and achieved a higher accuracy compared with conventional CNN during training. For datasets acquired using different settings, transfer learning can be applied to re-tune the model and improve accuracy.

KEY POINTS : • Deep learning can be applied to differentiate breast cancer molecular subtypes. • The recurrent neural network using CLSTM could track the change of signal intensity in DCE images, and achieved a higher accuracy compared with conventional CNN during training. • For datasets acquired using different scanners with different imaging protocols, transfer learning provided an efficient method to re-tune the classification model and improve accuracy.

Zhang Yang, Chen Jeon-Hor, Lin Yezhi, Chan Siwa, Zhou Jiejie, Chow Daniel, Chang Peter, Kwong Tiffany, Yeh Dah-Cherng, Wang Xinxin, Parajuli Ritesh, Mehta Rita S, Wang Meihao, Su Min-Ying

2020-Oct-01

Breast neoplasms, Machine learning, Magnetic resonance imaging, Receptor, ErbB-2, Receptors, estrogen

In European radiology ; h5-index 62.0

OBJECTIVE : To explore the application of deep learning in patients with primary osteoporosis, and to develop a fully automatic method based on deep convolutional neural network (DCNN) for vertebral body segmentation and bone mineral density (BMD) calculation in CT images.

MATERIALS AND METHODS : A total of 1449 patients were used for experiments and analysis in this retrospective study, who underwent spinal or abdominal CT scans for other indications between March 2018 and May 2020. All data was gathered from three different CT vendors. Among them, 586 cases were used for training, and other 863 cases were used for testing. A fully convolutional neural network, called U-Net, was employed for automated vertebral body segmentation. The manually sketched region of vertebral body was used as the ground truth for comparison. A convolutional neural network, called DenseNet-121, was applied for BMD calculation. The values post-processed by quantitative computed tomography (QCT) were identified as the standards for analysis.

RESULTS : Based on the diversity of CT vendors, all testing cases were split into three testing cohorts: Test set 1 (n = 463), test set 2 (n = 200), and test set 3 (n = 200). Automated segmentation correlated well with manual segmentation regarding four lumbar vertebral bodies (L1-L4): the minimum average dice coefficients for three testing sets were 0.823, 0.786, and 0.782, respectively. For testing sets from different vendors, the average BMDs calculated by automated regression showed high correlation (r > 0.98) and agreement with those derived from QCT.

CONCLUSIONS : A deep learning-based method could achieve fully automatic identification of osteoporosis, osteopenia, and normal bone mineral density in CT images.

KEY POINTS : • Deep learning can perform accurate fully automated segmentation of lumbar vertebral body in CT images. • The average BMDs obtained by deep learning highly correlates with ones derived from QCT. • The deep learning-based method could be helpful for clinicians in opportunistic osteoporosis screening in spinal or abdominal CT scans.

Fang Yijie, Li Wei, Chen Xiaojun, Chen Keming, Kang Han, Yu Pengxin, Zhang Rongguo, Liao Jianwei, Hong Guobin, Li Shaolin

2020-Oct-01

Bone density, Deep learning, Osteoporosis, Spine, Tomography, X-ray computed

In Human molecular genetics ; h5-index 81.0

Laufer Benjamin I, Hwang Hyeyeon, Jianu Julia M, Mordaunt Charles E, Korf Ian F, Hertz-Picciotto Irva, LaSalle Janine M

2020-Oct-01

In Anatolian journal of cardiology

Badano Luigi P, Keller Daria M, Muraru Denisa, Torlasco Camilla, Parati Gianfranco

2020-Oct

In eLife

van Gurp Sander, Hoog Jochen, Kalenscher Tobias, van Wingerden Marijn

2020-Oct-01

associative learning, neuroscience, rat, unblocking, vicarious reward