MRI-SegFlow: a novel unsupervised deep learning pipeline enabling accurate vertebral segmentation of MRI images.

Most deep learning based vertebral segmentation methods require laborious manual labelling tasks. We aim to establish an unsupervised deep learning pipeline for vertebral segmentation of MR images. We integrate the sub-optimal segmentation results produced by a rule-based method with a unique voting mechanism to provide supervision in the training process for the deep learning model. Preliminary validation shows a high segmentation accuracy achieved by our method without relying on any manual labelling.The clinical relevance of this study is that it provides an efficient vertebral segmentation method with high accuracy. Potential applications are in automated pathology detection and vertebral 3D reconstructions for biomechanical simulations and 3D printing, facilitating clinical decision making, surgical planning and tissue engineering

Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks

This paper presents a method for automatic segmentation, localization, and identification of vertebrae in arbitrary 3D CT images. Many previous works do not perform the three tasks simultaneously even though requiring a priori knowledge of which part of the anatomy is visible in the 3D CT images. Our method tackles all these tasks in a single multi-stage framework without any assumptions. In the first stage, we train a 3D Fully Convolutional Networks to find the bounding boxes of the cervical, thoracic, and lumbar vertebrae. In the second stage, we train an iterative 3D Fully Convolutional Networks to segment individual vertebrae in the bounding box. The input to the second networks have an auxiliary channel in addition to the 3D CT images. Given the segmented vertebra regions in the auxiliary channel, the networks output the next vertebra. The proposed method is evaluated in terms of segmentation, localization, and identification accuracy with two public datasets of 15 3D CT images from the MICCAI CSI 2014 workshop challenge and 302 3D CT images with various pathologies introduced in [1]. Our method achieved a mean Dice score of 96%, a mean localization error of 8.3 mm, and a mean identification rate of 84%. In summary, our method achieved better performance than all existing works in all the three metrics

OncoSpineSeg: A Software Tool for a Manual Segmentation of Computed Tomography of the Spine on Cancer Patients

The organ most commonly affected by metastatic cancer is the skeleton, and spine is the site where it causes the highest morbidity. Computer-aided diagnosis (CAD) for detecting and assessing metastatic disease in bone or other spine disorders can assist physicians to perform their decision-making tasks. A precise segmentation of the spine is important as a first stage in any automatic diagnosis task. However, it is a challenging problem to segment correctly an affected spine, and it is a crucial step to assess quantitatively the results of segmentation by comparing them with the results of a manual segmentation, reviewed by one experienced radiologist. This chapter presents the design of a MATLAB-based software for the manual segmentation of the spine. The software tool has a simple and easy to use interface, and it works with either computed tomography or magnetic resonance imaging (MRI). A typical workflow includes loading the image volume, creating multi-planar reconstructions, manually contouring the vertebrae, spinal lesions, intervertebral discs and spinal canal with availability of different segmentation tools, classification of the bone into healthy bone, osteolytic metastases, osteoblastic metastases or mixed lesions, being also possible to classify an object as a false-positive and a 3D reconstruction of the segmented objects

Segmentación automática de la columna vertebral mediante un atlas probabilístico con un enfoque especial en la supresión de costillas

[ES] Puesto que la metástasis ósea es una patología vertebral de gran importancia, una segmentación precisa de los cuerpos vertebrales es el paso previo al análisis biomecánico que permita predecir el riesgo de fractura en vértebras metastásicas. Además, la localización exacta del canal vertebral es esencial en el proceso de radioterapia para evitar daños de la médula espinal, y un paso importante para automatizar la segmentación. Este Trabajo Final de Grado tiene como objetivo desarrollar un método automático para la detección y segmentación de las vértebras a través del análisis de Tomografía Computarizada utilizando un grupo de 21 pacientes con metástasis en la columna vertebral. Conseguir una segmentación automática de los cuerpos vertebrales es una tarea compleja debido a la presencia de las costillas en la región torácica. Como solución se han combinado un método Level-Set capaz de segmentar las vértebras y un atlas probabilístico para suprimir las costillas y automatizar el proceso. Para evaluar la segmentación se ha utilizado la distancia Hausdorff (HD) y el coeficiente Dice (DSC). Tras aplicar el atlas, la HD disminuye 11 mm de media y el DSC mejora un 1.3%. Los resultados demuestran que el atlas es capaz de detectar y suprimir las costillas adecuadamente.[EN] Since bone metastases is a relevant vertebral pathology, an accurate segmentation of the vertebral bodies is the previous step to biomechanical analysis to predict the risk of fracture in metastatic vertebrae. In addition, a proper location of the spinal canal is an essential process in radiotherapy processes to prevent spinal cord damages and a relevant step to automate the segmentation process. Aided by the Computerized Tomography technique, the target of this Final Degree Project is to model an automated method for the detection and segmentation of the spine and test it in a group of 21 patients with spinal metastases. To achieve an automatic segmentation of the vertebral bodies is a complex task due to the presence of the ribs in the thoracic region. As a solution, a Level-Set method used in the vertebrae segmentation process and a probabilistic atlas to suppress the ribs and automate the process have been combined. Both, the DICE similarity coefficient (DSC) and the Hausdorff (HD) distance have been used to evaluate the segmentation process. On average, HD decreases 11 mm and DSC improves 1.3% after applying the atlas. The results show that the atlas is able to detect and suppress the ribs properly.D' Ocón Alcañiz, V. (2019). Segmentación automática de la columna vertebral mediante un atlas probabilístico con un enfoque especial en la supresión de costillas. Universitat Politècnica de València. http://hdl.handle.net/10251/128515TFG

Automated detection, labelling and radiological grading of clinical spinal MRIs

Spinal magnetic resonance (MR) scans are a vital tool for diagnosing the cause of back pain for many diseases and conditions. However, interpreting clinically useful information from these scans can be challenging, time-consuming and hard to reproduce across different radiologists. In this paper, we alleviate these problems by introducing a multi-stage automated pipeline for analysing spinal MR scans. This pipeline first detects and labels vertebral bodies across several commonly used sequences (e.g. T1w, T2w and STIR) and fields of view (e.g. lumbar, cervical, whole spine). Using these detections it then performs automated diagnosis for several spinal disorders, including intervertebral disc degenerative changes in T1w and T2w lumbar scans, and spinal metastases, cord compression and vertebral fractures. To achieve this, we propose a new method of vertebrae detection and labelling, using vector fields to group together detected vertebral landmarks and a language-modelling inspired beam search to determine the corresponding levels of the detections. We also employ a new transformer-based architecture to perform radiological grading which incorporates context from multiple vertebrae and sequences, as a real radiologist would. The performance of each stage of the pipeline is tested in isolation on several clinical datasets, each consisting of 66 to 421 scans. The outputs are compared to manual annotations of expert radiologists, demonstrating accurate vertebrae detection across a range of scan parameters. Similarly, the model’s grading predictions for various types of disc degeneration and detection of spinal metastases closely match those of an expert radiologist. To aid future research, our code and trained models are made publicly available