Convolutional neural networks for segmentation of 49 selected bones in CT images show high reproducibility
Artikel i vetenskaplig tidskrift, 2017

Aim: An automated method to calculate Bone Scan Index (BSI) from bone scans has recently been established as a first imaging biomarker in patients with metastatic prostate cancer. BSI has shown to be an independent predictor of survival. PET/CT is more accurate than bone scans in detecting bone metastases.
We therefore decided to develop an automated PET/CT based imaging biomarker for assessment of tumor burden in bone. The aim of this project was to develop a method for automated segmentation and volume calculation of bones in CT images, which is the first step in the process of developing a PET/CT based imaging biomarker.
Materials and Methods: Convolutional neural networks (CNN) were trained to segment 49 selected bones (12 thoracic vertebrae, 5 lumbar vertebrae, sacrum, 2 hip bones, 24 ribs, 2 scapulae, 2 clavicles and the sternum) using manual segmentations in CT images from 23 patients performed by experienced image readers. Anatomical landmarks were detected using a CNN and pruned using a shape model.
These landmarks and the CT image were fed to a second CNN, segmenting the 49 selected bones. After the training process, the CNN segmented the bones in CT images in a separate validation group consisting of 46 patients with prostate cancer. All patients had undergone both 18F-Choline and 18F-NaF PET/CT within a time frame of 3 weeks as part of a previous research project. The two CT scans from each patient were segmented by the CNN and the two volumes of each bone were calculated.
Results: The total volume of the 49 bones was on average 3,086 mL in the 46 patients. The individual bones ranged in volume from 8 mL (left 12th rib) to 440 mL (left hip bone). The reproducibility measured as ratio volume difference/mean volume was on average less than 2% for all bones except for the ribs. The mean volumes, differences and reproducibility for the bones of five anatomical regions were as follow: thoracic vertebrae 39mL, 0.6mL, 1.5%; lumbar vertebra 71mL, 0.8 mL, 1.2%; sacrum, hip bones 386mL, 0.9mL, 0.3%; ribs 26mL, 2.0mL, 8.5%; scapulae, clavicles, sternum 97mL, -0.1mL, -0.4%.
Conclusion: Our CNN based method for automated segmentation of bones in CT images showed high reproducibility. A reproducible way to segment the skeleton and to measure the bone volume will be important in the development of a PET index relating volumes of abnormal PET tracer uptake to the bone volume.

Författare

May Sadik

Sahlgrenska universitetssjukhuset

Reza Kaboteh

Sahlgrenska universitetssjukhuset

Elin Trägårdh

Skånes universitetssjukhus (SUS)

Olof Enqvist

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Johannes Ulén

Eigenvision AB

Jane Angel Simonsen

Odense Universitetshospital

Mads Poulsen

Odense Universitetshospital

Poul Flemming Høilund-Carlsen

Odense Universitetshospital

Lars Edenbrandt

Sahlgrenska universitetssjukhuset

European Journal of Nuclear Medicine and Molecular Imaging

1619-7070 (ISSN) 1619-7089 (eISSN)

Vol. 44 Supplement 2

Ämneskategorier

Radiologi och bildbehandling

Medicinsk bildbehandling

Mer information

Senast uppdaterat

2022-04-04