Combining CGAN and Mil for Hotspot Segmentation in Bone Scintigraphy

Abstract

Bone scintigraphy is widely used to diagnose bone tumor and metastasis. Accurate hotspot segmentation from bone scintigraphy is of great importance for tumor metastasis diagnosis. In this paper, we propose a new framework to detect and extract hotspots in thoracic region by integrating the techniques of both conditional generative adversarial networks (cGAN) and multiple instance learning (MIL). We first use cGAN to train a generator, which can be applied to separate input bone scan image into four anatomical regions and provide location information. A multi-dimensional feature is constructed to integrate contrast, texture and location information. We then use MIL to train a patch-level classifier with this constructed feature. In hotspot segmentation, a hotspots probability map can be estimated with the patch-level classifier. The hotspot segmentation is performed with level set method, in which the hotspot boundary is initialized based on the hotspot probability map. We evaluate the proposed framework quantitatively on the hotspot dataset, and compare it with other methods.