Öz
Adrenal lesions are usually discovered incidentally during other health screenings and are usually benign. However, it is vital to take precautions when a malignant adrenal lesion is detected. Especially deep learning models developed in the last ten years give successful results on medical images. In this paper, adrenal lesion characterization on T1-weighted magnetic resonance abdomen images was aimed using convolutional neural network (CNN) which is one of the deep learning methods. Firstly, effects of important model parameters are assessed on performance of CNN, so optimum CNN model is obtained for classification of adrenal lesions. For a fixed number of convolution filters determined in the first stage of the study, CNN model implemented by different kernel sizes were trained. According to the best result obtained, this time the kernel size was kept constant, and experiments were made for different filter numbers. Finally, studies were carried out with CNN structures of different depths and the results were compared. As a result of the studies, when filter is selected as [5 20], the best results in the trainings conducted with a single-block CNN structure are obtained 0.97, 0.90, 0.98, 0.90, 0.90, and 0.94, for accuracy, sensitivity, specificity, precision, F1-score, and AUC score, respectively. The study was compared with the studies in the literature, and it was seen that it was superior to them.
Anahtar Kelimeler
Abdomen Adrenal Lesion Classification Convolutional Neural Network Deep Learning Magnetic Resonance
Destekleyen Kurum
Konya Teknik Üniversitesi Öğretim Üyesi Yetiştirme Programı
Proje Numarası
2018-OYP-033
Kaynakça
- Albarqouni, S., Baur, C., Achilles, F., Belagiannis, V., Demirci, S., & Navab, N. (2016). Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE transactions on medical imaging, 35(5), 1313-1321.
- Alex, V., KP, M. S., Chennamsetty, S. S., & Krishnamurthi, G. (2017). Generative adversarial networks for brain lesion detection. Paper presented at the Medical Imaging 2017: Image Processing.
- Chen, H., Dou, Q., Yu, L., Qin, J., & Heng, P.-A. (2018). VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images. NeuroImage, 170, 446-455.
- Dhungel, N., Carneiro, G., & Bradley, A. P. (2015, 2015//). Deep Learning and Structured Prediction for the Segmentation of Mass in Mammograms. Paper presented at the Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015, Cham.
- Elmohr, M., Fuentes, D., Habra, M., Bhosale, P., Qayyum, A., Gates, E., . . . Elsayes, K. (2019). Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT. Clinical radiology, 74(10), 818. e811-818. e817.
- Fassnacht, M., Arlt, W., Bancos, I., Dralle, H., Newell-Price, J., Sahdev, A., . . . Dekkers, O. M. (2016). Management of adrenal incidentalomas: European society of endocrinology clinical practice guideline in collaboration with the European network for the study of adrenal tumors. European journal of endocrinology, 175(2), G1-G34.
- Guan, S., & Loew, M. (2017). Breast Cancer Detection Using Transfer Learning in Convolutional Neural Networks. Paper presented at the 2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).
- Kang, J., & Gwak, J. (2019). Ensemble of instance segmentation models for polyp segmentation in colonoscopy images. IEEE Access, 7, 26440-26447.
- Koyuncu, H., Ceylan, R., Asoglu, S., Cebeci, H., & Koplay, M. (2019). An extensive study for binary characterisation of adrenal tumours. Medical & biological engineering & computing, 57(4), 849-862.
- Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Paper presented at the Advances in neural information processing systems.
- Li, Q., Yang, G., Chen, Z., Huang, B., Chen, L., Xu, D., . . . Wang, T. (2017, 14-16 Oct. 2017). Colorectal polyp segmentation using a fully convolutional neural network. Paper presented at the 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).
- Li, X., Guindani, M., Ng, C., & Hobbs, B. (2017). Classification of adrenal lesions through spatial Bayesian modeling of GLCM. Paper presented at the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).
- Liu, H., Guan, X., Xu, B., Zeng, F., Chen, C., Yin, H. L., . . . Chen, B. T. (2022). Computed Tomography-Based Machine Learning Differentiates Adrenal Pheochromocytoma From Lipid-Poor Adenoma. Frontiers in endocrinology, 13, 833413.
- Moeskops, P., Veta, M., Lafarge, M. W., Eppenhof, K. A., & Pluim, J. P. (2017). Adversarial training and dilated convolutions for brain MRI segmentation. In Deep learning in medical image analysis and multimodal learning for clinical decision support (pp. 56-64): Springer.
- Nguyen, Q., & Lee, S.-W. (2018). Colorectal segmentation using multiple encoder-decoder network in colonoscopy images. Paper presented at the 2018 IEEE first international conference on artificial intelligence and knowledge engineering (AIKE).
- Romeo, V., Maurea, S., Cuocolo, R., Petretta, M., Mainenti, P. P., Verde, F., . . . Brunetti, A. (2018). Characterization of adrenal lesions on unenhanced MRI using texture analysis: a machine‐learning approach. Journal of Magnetic Resonance Imaging, 48(1), 198-204.
Öz
Adrenal lesions are usually discovered incidentally during other health screenings and are usually benign. However, it is vital to take precautions when a malignant adrenal lesion is detected. Especially deep learning models developed in the last ten years give successful results on medical images. In this paper, adrenal lesion characterization on T1-weighted magnetic resonance abdomen images was aimed using convolutional neural network (CNN) which is one of the deep learning methods. Firstly, effects of important model parameters are assessed on performance of CNN, so optimum CNN model is obtained for classification of adrenal lesions. For a fixed number of convolution filters determined in the first stage of the study, CNN model implemented by different kernel sizes were trained. According to the best result obtained, this time the kernel size was kept constant, and experiments were made for different filter numbers. Finally, studies were carried out with CNN structures of different depths and the results were compared. As a result of the studies, when filter is selected as [5 20], the best results in the trainings conducted with a single-block CNN structure are obtained 0.97, 0.90, 0.98, 0.90, 0.90, and 0.94, for accuracy, sensitivity, specificity, precision, F1-score, and AUC score, respectively. The study was compared with the studies in the literature, and it was seen that it was superior to them.
Anahtar Kelimeler
Abdomen Adrenal Lesion Classification Convolutional Neural Network Deep Learning Magnetic Resonance
Proje Numarası
2018-OYP-033
Kaynakça
- Albarqouni, S., Baur, C., Achilles, F., Belagiannis, V., Demirci, S., & Navab, N. (2016). Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE transactions on medical imaging, 35(5), 1313-1321.
- Alex, V., KP, M. S., Chennamsetty, S. S., & Krishnamurthi, G. (2017). Generative adversarial networks for brain lesion detection. Paper presented at the Medical Imaging 2017: Image Processing.
- Chen, H., Dou, Q., Yu, L., Qin, J., & Heng, P.-A. (2018). VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images. NeuroImage, 170, 446-455.
- Dhungel, N., Carneiro, G., & Bradley, A. P. (2015, 2015//). Deep Learning and Structured Prediction for the Segmentation of Mass in Mammograms. Paper presented at the Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015, Cham.
- Elmohr, M., Fuentes, D., Habra, M., Bhosale, P., Qayyum, A., Gates, E., . . . Elsayes, K. (2019). Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT. Clinical radiology, 74(10), 818. e811-818. e817.
- Fassnacht, M., Arlt, W., Bancos, I., Dralle, H., Newell-Price, J., Sahdev, A., . . . Dekkers, O. M. (2016). Management of adrenal incidentalomas: European society of endocrinology clinical practice guideline in collaboration with the European network for the study of adrenal tumors. European journal of endocrinology, 175(2), G1-G34.
- Guan, S., & Loew, M. (2017). Breast Cancer Detection Using Transfer Learning in Convolutional Neural Networks. Paper presented at the 2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).
- Kang, J., & Gwak, J. (2019). Ensemble of instance segmentation models for polyp segmentation in colonoscopy images. IEEE Access, 7, 26440-26447.
- Koyuncu, H., Ceylan, R., Asoglu, S., Cebeci, H., & Koplay, M. (2019). An extensive study for binary characterisation of adrenal tumours. Medical & biological engineering & computing, 57(4), 849-862.
- Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Paper presented at the Advances in neural information processing systems.
- Li, Q., Yang, G., Chen, Z., Huang, B., Chen, L., Xu, D., . . . Wang, T. (2017, 14-16 Oct. 2017). Colorectal polyp segmentation using a fully convolutional neural network. Paper presented at the 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).
- Li, X., Guindani, M., Ng, C., & Hobbs, B. (2017). Classification of adrenal lesions through spatial Bayesian modeling of GLCM. Paper presented at the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).
- Liu, H., Guan, X., Xu, B., Zeng, F., Chen, C., Yin, H. L., . . . Chen, B. T. (2022). Computed Tomography-Based Machine Learning Differentiates Adrenal Pheochromocytoma From Lipid-Poor Adenoma. Frontiers in endocrinology, 13, 833413.
- Moeskops, P., Veta, M., Lafarge, M. W., Eppenhof, K. A., & Pluim, J. P. (2017). Adversarial training and dilated convolutions for brain MRI segmentation. In Deep learning in medical image analysis and multimodal learning for clinical decision support (pp. 56-64): Springer.
- Nguyen, Q., & Lee, S.-W. (2018). Colorectal segmentation using multiple encoder-decoder network in colonoscopy images. Paper presented at the 2018 IEEE first international conference on artificial intelligence and knowledge engineering (AIKE).
- Romeo, V., Maurea, S., Cuocolo, R., Petretta, M., Mainenti, P. P., Verde, F., . . . Brunetti, A. (2018). Characterization of adrenal lesions on unenhanced MRI using texture analysis: a machine‐learning approach. Journal of Magnetic Resonance Imaging, 48(1), 198-204.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Elektrik Mühendisliği |
| Bölüm | Makaleler |
| Yazarlar | |
| Proje Numarası | 2018-OYP-033 |
| Yayımlanma Tarihi | 31 Aralık 2022 |
| Gönderilme Tarihi | 7 Aralık 2022 |
| Yayımlandığı Sayı | Yıl 2022 Cilt: 14 Sayı: 3 |