Automated Segmentation of Thyroid Nodule, Gland, and Cystic Components From Ultrasound Images Using Deep Learning

Abstract

Sonographic features associated with margins, shape, size, and volume of thyroid nodules are used to assess their risk of malignancy. Automatically segmenting nodules from normal thyroid gland would enable an automated estimation of these features. A novel multi-output convolutional neural network algorithm with dilated convolutional layers is presented to segment thyroid nodules, cystic components inside the nodules, and normal thyroid gland from clinical ultrasound B-mode scans. A prospective study was conducted, collecting data from 234 patients undergoing a thyroid ultrasound exam before biopsy. The training and validation sets encompassed 188 patients total; the testing set consisted of 48 patients. The algorithm effectively segmented thyroid anatomy into nodules, normal gland, and cystic components. The algorithm achieved a mean Dice coefficient of 0.76, a mean true positive fraction of 0.90, and a mean false positive fraction of 1.61×10^-6. The values are on par with a conventional seeded algorithm. The proposed algorithm eliminates the need for a seed in the segmentation process, thus automatically detecting and segmenting the thyroid nodules and cystic components. The detection rate for thyroid nodules and cystic components was 82% and 44%, respectively. The inference time per image, per fold was 107ms. The mean error in volume estimation of thyroid nodules for five select cases was 7.47%. The algorithm can be used for detection, segmentation, size estimation, volume estimation, and generating thyroid maps for thyroid nodules. The algorithm has applications in point of care, mobile health monitoring, improving workflow, reducing localization time, and assisting sonographers with limited expertise.

Keywords: Deep learning; segmentation; thyroid nodule; thyroid nodule volume; ultrasound.

Fig. 1.

Architecture of a multi-prong convolutional neural network. N is the number of filters. Input is a B-mode image and output is the normal thyroid, thyroid nodule, and cyst mask.

Fig. 2.

Training and validation characteristics of loss and accuracy for all stages of training. The blue line is the mean results on the training set during each epoch and the red line is the results on the validation at the end of each epoch.

Fig. 3.

Boxplots showing the Dice coefficient versus different suspicion levels using the multi-prong convolutional neural network (MPCNN) and distance regularized level set (DRLS) algorithms for (a) thyroid nodules, (b) normal thyroid, and (c) cysts.

Fig. 4.

Boxplots showing the true positive fraction versus different suspicion levels using the multi-prong convolutional neural network (MPCNN) and distance regularized level set (DRLS) algorithms for (a) thyroid nodules, (b) normal thyroid, and (c) cysts.

Fig. 5.

Boxplots showing the false positive fraction versus different suspicion levels using the multi-prong convolutional neural network (MPCNN) and distance regularized level set (DRLS) algorithms for (a) thyroid nodules, (b) normal thyroid, and (c) cysts.

Fig. 6.

Boxplots showing the Dice coefficient versus pathology using the multi-prong convolutional neural network (MPCNN) and distance regularized level set (DRLS) algorithms for (a) thyroid nodules and (b) normal thyroid.

Fig. 7.

Boxplots showing the true positive fraction versus pathology using the multi-prong convolutional neural network (MPCNN) and distance regularized level set (DRLS) algorithms for (a) thyroid nodules and (b) normal thyroid.

Fig. 8.

Boxplots showing the false positive fraction versus pathology using the multi-prong convolutional neural network (MPCNN) and distance regularized level set (DRLS) algorithms for (a) thyroid nodules and (b) normal thyroid.

Fig. 9.

Boxplots showing the Dice coefficient versus orientation using the multi-prong convolutional neural network (MPCNN) and distance regularized level set (DRLS) algorithms for (a) thyroid nodules, (b) normal thyroid, and (c) cysts.

Fig. 10.

Boxplots showing the true positive fraction versus orientation using the multi-prong convolutional neural network (MPCNN) and distance regularized level set (DRLS) algorithms for (a) thyroid nodules, (b) normal thyroid, and (c) cysts.

Fig. 11.

Boxplots showing the false positive fraction versus orientation using the multi-prong convolutional neural network (MPCNN) and distance regularized level set (DRLS) algorithms for (a) thyroid nodules, (b) normal thyroid, and (c) cysts.

Fig. 12.

Dice coefficient values versus number of models along with error bars for (a) thyroid nodules, (b) normal thyroid, and (c) cysts.

Fig. 13.

(a) B-mode image of a benign thyroid nodule. (b) Manual segmentation by a board-certified sonographer with thyroid nodule in red and normal thyroid in blue. (c) Predicted boundaries using the MPCNN algorithm.

Fig. 14.

(a) B-mode image of a benign thyroid nodule with degenerative changes. (b) Manual segmentation by a board-certified sonographer with the thyroid nodule in red, normal thyroid in blue, and cyst in green. (c) Predicted boundaries using the multi-prong convolutional neural network with the thyroid nodule in red, normal thyroid in blue, and cyst in green. A mean Dice coefficient of 0.95 was achieved.

Fig. 15.

(a) B-mode image of a benign thyroid nodule with degenerative changes. (b) Manual segmentation by a board-certified sonographer with thyroid nodule in red, normal thyroid in blue, and cysts in green (c) Predicted boundaries using the multi-prong convolutional neural network with thyroid nodule in red, normal thyroid in blue, and cysts in green. A mean Dice coefficient of 0.93 was achieved.

Fig. 16.

(a) B-mode image of a suspicious thyroid nodule with cytological features suspicious for a follicular neoplasm. (b) Manual segmentation by a board-certified sonographer with thyroid nodule in red and normal thyroid in blue. (c) Predicted boundaries using the multi-prong convolutional neural network with thyroid nodule in red and normal thyroid in blue. A mean Dice coefficient of 0.94 was achieved.

Fig. 17.

(a) B-mode image of a malignant thyroid nodule with cytological features consistent with papillary thyroid carcinoma. (b) Manual segmentation by a board-certified sonographer with thyroid nodule in red and normal thyroid in blue. (c) Predicted boundaries using the multi-prong convolutional neural network with thyroid nodule in red and normal thyroid in blue. A mean Dice coefficient of 0.94 was achieved.

Fig. 18.

shows an ROC curve for the thyroid, nodules and cyst classes