TY - GEN
T1 - Identifying Thyroid Nodules in Ultrasound Images Through Segmentation-Guided Discriminative Localization
AU - Lu, Jintao
AU - Ouyang, Xi
AU - Liu, Tianjiao
AU - Shen, Dinggang
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - In this paper, we propose a novel segmentation-guided network for thyroid nodule identification from ultrasound images. Accurate diagnosis of thyroid nodules through ultrasound images is significant for cancer detection at the early stage. Many Computer-Aided Diagnose (CAD) systems for this task ignore the inherent correlation between nodule segmentation task and classification task (i.e. cancer grading). Actually, segmentation results could be used as localization cues of thyroid nodules for facilitating their classifications as benign or malignant. Accordingly, we propose a two-stage thyroid nodule diagnosis method through 1) nodule segmentation and 2) segmentation-guided diagnosis. Specifically, in the segmentation stage, we use an ensemble strategy to integrate segmentations from diverse segmentation networks. Then, in the classification stage, the obtained segmentation result is integrated as additional information along with its corresponding original ultrasound images as the input of the classification network. Meanwhile, the segmentation result is further served as guidance to refine the attention map of the features used for classification. Our method is applied to the TN-SCUI 2020, a MICCAI 2020 Challenge, with the largest set of thyroid nodule ultrasound images according to our knowledge. Our method achieved the 2nd place in its classification challenge.
AB - In this paper, we propose a novel segmentation-guided network for thyroid nodule identification from ultrasound images. Accurate diagnosis of thyroid nodules through ultrasound images is significant for cancer detection at the early stage. Many Computer-Aided Diagnose (CAD) systems for this task ignore the inherent correlation between nodule segmentation task and classification task (i.e. cancer grading). Actually, segmentation results could be used as localization cues of thyroid nodules for facilitating their classifications as benign or malignant. Accordingly, we propose a two-stage thyroid nodule diagnosis method through 1) nodule segmentation and 2) segmentation-guided diagnosis. Specifically, in the segmentation stage, we use an ensemble strategy to integrate segmentations from diverse segmentation networks. Then, in the classification stage, the obtained segmentation result is integrated as additional information along with its corresponding original ultrasound images as the input of the classification network. Meanwhile, the segmentation result is further served as guidance to refine the attention map of the features used for classification. Our method is applied to the TN-SCUI 2020, a MICCAI 2020 Challenge, with the largest set of thyroid nodule ultrasound images according to our knowledge. Our method achieved the 2nd place in its classification challenge.
KW - Attention
KW - Identification
KW - Thyroid nodule
KW - Ultrasound
UR - http://www.scopus.com/inward/record.url?scp=85104413805&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-71827-5_18
DO - 10.1007/978-3-030-71827-5_18
M3 - Conference contribution
AN - SCOPUS:85104413805
SN - 9783030718268
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 135
EP - 144
BT - Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data - MICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, Held in Conjunction with MICCAI 2020, Proceedings
A2 - Shusharina, Nadya
A2 - Heinrich, Mattias P.
A2 - Huang, Ruobing
PB - Springer Science and Business Media Deutschland GmbH
T2 - Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images Challenge, ABCs 2020, Learn2Reg Challenge, L2R 2020 and Thyroid Nodule Segmentation and Classification in Ultrasound Images Challenge, TN-SCUI 2020 held in conjunction with 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Y2 - 4 October 2020 through 8 October 2020
ER -