Bone Metastasis Detection in the Chest and Pelvis from a Whole-Body Bone Scan Using Deep Learning and a Small Dataset

• Main Authors: Da-Chuan Cheng, Chia-Chuan Liu, Te-Chun Hsieh, Kuo-Yang Yen, Chia-Hung Kao
• Format: Article
• Language: English
• Published: MDPI AG 2021-05-01
• Series: Electronics
• Subjects: bone metastasis; deep learning; hard example mining
• Online Access: https://www.mdpi.com/2079-9292/10/10/1201

The aim of this study was to establish an early diagnostic system for the identification of the bone metastasis of prostate cancer in whole-body bone scan images by using a deep convolutional neural network (D-CNN). The developed system exhibited satisfactory performance for a small dataset containing 205 cases, 100 of which were of bone metastasis. The sensitivity and precision for bone metastasis detection and classification in the chest were 0.82 ± 0.08 and 0.70 ± 0.11, respectively. The sensitivity and specificity for bone metastasis classification in the pelvis were 0.87 ± 0.12 and 0.81 ± 0.11, respectively. We propose the use of hard example mining for increasing the sensitivity and precision of the chest D-CNN. The developed system has the potential to provide a prediagnostic report for physicians’ final decisions.