Title:mSegResRF-SPECT: A Novel Joint Classification Model of Whole Body Bone
Scan Images for Bone Metastasis Diagnosis
Volume: 20
Author(s): Bangning Ji, Gang He*, Jun Wen, Zhengguo Chen and Ling Zhao
Affiliation:
- Southwest University of Science and Technology, Mianyang, China
- NHC Key Laboratory of NuclearTechnology Medical Transformation (Mianyang Central Hospital), Mianyang, China
Keywords:
Whole body bone scan, Single-photon emission computed tomography, Joint model, Deep learning, Random forest, Bone metastasis.
Abstract:
Background:
Whole-body bone scanning is a nuclear medicine technique with high sensitivity used for the diagnosis of bone-related diseases [e.g., bone
metastases] that can be obtained by positron emission tomography (PET) or single-photon emission computed tomography[SPECT] imaging,
depending on the different radiopharmaceuticals used. In contrast to the high sensitivity of the bone scan, it has low specificity, which leads to
misinterpretation, causing adverse effects of unwarranted intervention or interruption to timely treatment.
Objective:
To address this problem, this paper proposes a joint model called mSegResRF-SPECT, which accomplishes for the first time the task of classifying
whole-body bone scan images on a public SPECT dataset [BS-80K] for the diagnosis of bone metastases.
Methods:
The mSegResRF-SPECT adopts a multi-bone region segmentation algorithm to segment the whole body image into 13 regions, ResNet34 as an
extractor to extract the regional features, and a random forest algorithm as a classifier.
Results:
The experimental results of the proposed model show that the average accuracy, sensitivity, and F1 score of the model on the BS-80K dataset
reached SOTA.
Conclusion:
The proposed method presents a promising solution for better bone scan classification methods.
