A Deep Regression Model for Seed Identification in Prostate Brachytherapy

TL;DR

This paper introduces a deep learning-based regression model for automatic seed identification in prostate brachytherapy CT images, effectively handling artifacts and overlaps, and outperforming existing commercial software.

Contribution

The paper presents a novel 3D deep fully convolutional regression model that improves seed detection accuracy in challenging CT images for prostate brachytherapy.

Findings

Achieved 94.1% seed detection accuracy in testing.

Outperformed commercial seed finder software by 16%.

Validated on a large clinical database with 7820 seeds.

Abstract

Post-implant dosimetry (PID) is an essential step of prostate brachytherapy that utilizes CT to image the prostate and allow the location and dose distribution of the radioactive seeds to be directly related to the actual prostate. However, it it a very challenging task to identify these seeds in CT images due to the severe metal artifacts and high-overlapped appearance when multiple seeds clustered together. In this paper, we propose an automatic and efficient algorithm based on 3D deep fully convolutional network for identifying implanted seeds in CT images. Our method models the seed localization task as a supervised regression problem that projects the input CT image to a map where each element represents the probability that the corresponding input voxel belongs to a seed. This deep regression model significantly suppresses image artifacts and makes the post-processing much easier and more controllable. The proposed method is validated on a large clinical database with 7820 seeds in 100 patients, in which 5534 seeds from 70 patients were used for model training and validation. Our method correctly detected 2150 of 2286 (94.1%) seeds in the 30 testing patients, yielding 16% improvement as compared to a widely-used commercial seed finder software (VariSeed, Varian, Palo Alto, CA).