Interactive segmentation for accurately isolating metastatic lesions from low-resolution, large-size bone scintigrams.

Objective.Bone is a common site for the metastasis of malignant tumors, and single photon emission computed tomography (SPECT) is widely used to detect these metastases. Accurate delineation of metastatic bone lesions in SPECT images is essential for developing treatment plans. However, current clinical practices rely on manual delineation by physicians, which is prone to variability and subjective interpretation. While computer-aided diagnosis systems have the potential to improve diagnostic efficiency, fully automated segmentation approaches frequently suffer from high false positive rates, limiting their clinical utility.Approach.This study proposes an interactive segmentation framework for SPECT images, leveraging the deep convolutional neural networks to enhance segmentation accuracy. The proposed framework incorporates a U-shaped backbone network that effectively addresses inter-patient variability, along with an interactive attention module that enhances feature extraction in densely packed bone regions.Main results.Extensive experiments using clinical data validate the effectiveness of the proposed framework. Furthermore, a prototype tool was developed based on this framework to assist in the clinical segmentation of metastatic bone lesions and to support the creation of a large-scale dataset for bone metastasis segmentation.Significance.In this study, we proposed an interactive segmentation framework for metastatic lesions in bone scintigraphy to address the challenging task of labeling low-resolution, large-size SPECT bone scans. The experimental results show that the model can effectively segment the bone metastases of lung cancer interactively. In addition, the prototype tool developed based on the model has certain clinical application value.