BS-80K: The first large open-access dataset of bone scan images

Abstract

Background: Radionuclide bone scanning is one of the most common tools in the inspection of bone metastasis. Conventionally, the analysis of bone scan image is derived from manual diagnosing. However, this task requires extensive subjective diagnostic experience and is extremely time-consuming. To this end, a series of studies concerning computer-aided diagnosis via machine learning tools have been proposed. Although some inspiring progress has been achieved, the implemented bone scan image datasets in these research areas are generally too small, private or non-general, which limits their practical significance and impedes the follow-up research.

Method: To address this issue, we present a large, publicly available and general dataset consisting of 82544 bone scan images associated with 3247 patients from West China Hospital, named BS-80K. In BS-80K, each patient provides two whole bone scan images corresponding to the anterior view (ANT) and the posterior view (POST). For each view, there are 13 region-wise slices of the body parts susceptible to bone metastasis. Based on an authorized original labeling criterion, labels annotated by experienced specialists are offered with the images. Moreover, within each whole body image, multiple bounding boxes containing suspectable hot spots and their annotations are supplied as well. All images in BS-80K have been de-identified to protect patients' privacy.

Results: Based on 6 popular deep learning models for classification and object detection, we provide the benchmark for a number of computer-aided medical tasks, including general bone metastasis prediction and object detection for whole body images, and specific bone metastasis prediction for different body parts. According to extensive experiments, the adopted classification models achieve remarkable results in accuracy and specificity (around 95%) on most metastasis prediction tasks, which are approximate to the average ability of corresponding specialists. As for the object detection task, the best average precision of the adopted models reaches 0.2484 and the lowest is 0.1334.

Discussion: Through the comparison of metastasis prediction performance between the benchmark and related work, we observe that the widely used models trained by BS-80K achieve significantly better results than the elaborately designed models trained by smaller datasets. This indicates that with the large amount of data, BS-80K has great potential to galvanize the research about computer-aided analysis on bone scan image.

Conclusion: To the best of our knowledge, BS-80K is the first large publicly available dataset of bone scanning, which favors a wide range of research on computer-aided bone metastasis diagnosis. The full dataset is now available at https://drive.google.com/drive/folders/1DOBkLXgQeREQjF-nQIGNBBzPCb5s7RNu?usp=sharing.

Keywords: Benchmark; Bone metastasis; Bone scintigraphy; Computer-aided diagnosis; Dataset.