Segmentation and Identification of Vertebrae in CT Scans Using CNN, k-Means Clustering and k-NN

Informatics

Antonio Brunetti¹  Domenico Buongiorno¹  Nicola Altini¹  Sardar Mehboob Hussain¹  Giuseppe De Giosa¹  Elena Sibilano¹  Berardino Prencipe¹  Andrea Guerriero¹  Vitoantonio Bevilacqua¹  Nicola Fragasso¹  Claudia Coscia¹  Gioacchino Brunetti²  Vito Triggiani²  Ilaria Sabina Tatò³ 

[1]Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, 70126 Bari, BA, Italy

[2]Masmec Biomed SpA, Via delle Violette n.14, 70026 Modugno, BA, Italy

[3]Medica Sud s.r.l., Viale della Resistenza n.82, 70125 Bari, BA, Italy

关键词: 3D convolutional neural network;    k-means clustering;    k-nearest neighbors;    vertebrae segmentation;    vertebrae identification;    computed tomography;   

DOI  :  10.3390/informatics8020040

来源: DOAJ

【 摘 要 】

The accurate segmentation and identification of vertebrae presents the foundations for spine analysis including fractures, malfunctions and other visual insights. The large-scale vertebrae segmentation challenge (VerSe), organized as a competition at the Medical Image Computing and Computer Assisted Intervention (MICCAI), is aimed at vertebrae segmentation and labeling. In this paper, we propose a framework that addresses the tasks of vertebrae segmentation and identification by exploiting both deep learning and classical machine learning methodologies. The proposed solution comprises two phases: a binary fully automated segmentation of the whole spine, which exploits a 3D convolutional neural network, and a semi-automated procedure that allows locating vertebrae centroids using traditional machine learning algorithms. Unlike other approaches, the proposed method comes with the added advantage of no requirement for single vertebrae-level annotations to be trained. A dataset of 214 CT scans has been extracted from VerSe’20 challenge data, for training, validating and testing the proposed approach. In addition, to evaluate the robustness of the segmentation and labeling algorithms, 12 CT scans from subjects affected by severe, moderate and mild scoliosis have been collected from a local medical clinic. On the designated test set from Verse’20 data, the binary spine segmentation stage allowed to obtain a binary Dice coefficient of 89.17%, whilst the vertebrae identification one reached an average multi-class Dice coefficient of 90.09%. In order to ensure the reproducibility of the algorithms hereby developed, the code has been made publicly available.

【 授权许可】

Unknown