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

Altini, Nicola; Giosa, Giuseppe De; Fragasso, Nicola; Coscia, Claudia; Sibilano, Elena; Prencipe, Berardino; Hussain, Sardar Mehboob; Brunetti, Antonio; Buongiorno, Domenico; Guerriero, Andrea; Tatò, Ilaria Sabina; Brunetti, Giuseppe; Triggiani, Vito; Bevilacqua, Vitoantonio

doi:10.3390/informatics8020040

Cited by 31 publications

(17 citation statements)

References 40 publications

(64 reference statements)

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“…The encoder–decoder structures have been implemented in different convolutional network architectures, including SegNet [ 30 ], U-Net [ 31 ], U-Net 3D [ 32 ], and V-Net [ 33 ]. Besides prostate segmentation, applications in medical imaging tasks of those architectures encompass liver vessels delineation [ 34 ], segments classification [ 35 ], lung COVID-19 lesions segmentation [ 36 ], and vertebrae segmentation [ 37 ].…”

Section: Methodsmentioning

confidence: 99%

A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net

et al. 2022

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In prostate cancer, fusion biopsy, which couples magnetic resonance imaging (MRI) with transrectal ultrasound (TRUS), poses the basis for targeted biopsy by allowing the comparison of information coming from both imaging modalities at the same time. Compared with the standard clinical procedure, it provides a less invasive option for the patients and increases the likelihood of sampling cancerous tissue regions for the subsequent pathology analyses. As a prerequisite to image fusion, segmentation must be achieved from both MRI and TRUS domains. The automatic contour delineation of the prostate gland from TRUS images is a challenging task due to several factors including unclear boundaries, speckle noise, and the variety of prostate anatomical shapes. Automatic methodologies, such as those based on deep learning, require a huge quantity of training data to achieve satisfactory results. In this paper, the authors propose a novel optimization formulation to find the best superellipse, a deformable model that can accurately represent the prostate shape. The advantage of the proposed approach is that it does not require extensive annotations, and can be used independently of the specific transducer employed during prostate biopsies. Moreover, in order to show the clinical applicability of the method, this study also presents a module for the automatic segmentation of the prostate gland from MRI, exploiting the nnU-Net framework. Lastly, segmented contours from both imaging domains are fused with a customized registration algorithm in order to create a tool that can help the physician to perform a targeted prostate biopsy by interacting with the graphical user interface.

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Section: Methodsmentioning

confidence: 99%

A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net

et al. 2022

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“…For example, Zhou et al [ 15 ] assume that the first sacrum vertebra (S1) is within the image while Yi et al [ 16 ] assume that always the same vertebrae are visible. The model of Altini et al [ 17 ] on the other hand requires manual input with meta-information about the first visible vertebra. Other approaches make assumptions about the shape of the spine [ 18 ] and therefore do not work well in pathological cases where the spine is deformed.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Domain Adaptation for Vertebrae Detection and Identification in 3D CT Volumes Using a Domain Sanity Loss

et al. 2022

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A variety of medical computer vision applications analyze 2D slices of computed tomography (CT) scans, whereas axial slices from the body trunk region are usually identified based on their relative position to the spine. A limitation of such systems is that either the correct slices must be extracted manually or labels of the vertebrae are required for each CT scan to develop an automated extraction system. In this paper, we propose an unsupervised domain adaptation (UDA) approach for vertebrae detection and identification based on a novel Domain Sanity Loss (DSL) function. With UDA the model’s knowledge learned on a publicly available (source) data set can be transferred to the target domain without using target labels, where the target domain is defined by the specific setup (CT modality, study protocols, applied pre- and processing) at the point of use (e.g., a specific clinic with its specific CT study protocols). With our approach, a model is trained on the source and target data set in parallel. The model optimizes a supervised loss for labeled samples from the source domain and the DSL loss function based on domain-specific “sanity checks” for samples from the unlabeled target domain. Without using labels from the target domain, we are able to identify vertebra centroids with an accuracy of 72.8%. By adding only ten target labels during training the accuracy increases to 89.2%, which is on par with the current state-of-the-art for full supervised learning, while using about 20 times less labels. Thus, our model can be used to extract 2D slices from 3D CT scans on arbitrary data sets fully automatically without requiring an extensive labeling effort, contributing to the clinical adoption of medical imaging by hospitals.

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“…In this work, in order to realize the segmentation of lung parenchyma and lesions, we trained two CNN models based on the V-Net architecture, but considering the 2.5D and 2D variants with the same Dice loss formulation provided by Altini et al [36,37]. For the task of lesion segmentation, two classes were considered: GGO and LC, as also detailed in Section 2.…”

Section: Semantic Segmentationmentioning

confidence: 99%

Lung Segmentation and Characterization in COVID-19 Patients for Assessing Pulmonary Thromboembolism: An Approach Based on Deep Learning and Radiomics

et al. 2021

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The COVID-19 pandemic is inevitably changing the world in a dramatic way, and the role of computed tomography (CT) scans can be pivotal for the prognosis of COVID-19 patients. Since the start of the pandemic, great care has been given to the relationship between interstitial pneumonia caused by the infection and the onset of thromboembolic phenomena. In this preliminary study, we collected n = 20 CT scans from the Polyclinic of Bari, all from patients positive with COVID-19, nine of which developed pulmonary thromboembolism (PTE). For eight CT scans, we obtained masks of the lesions caused by the infection, annotated by expert radiologists; whereas for the other four CT scans, we obtained masks of the lungs (including both healthy parenchyma and lesions). We developed a deep learning-based segmentation model that utilizes convolutional neural networks (CNNs) in order to accurately segment the lung and lesions. By considering the images from publicly available datasets, we also realized a training set composed of 32 CT scans and a validation set of 10 CT scans. The results obtained from the segmentation task are promising, allowing to reach a Dice coefficient higher than 97%, posing the basis for analysis concerning the assessment of PTE onset. We characterized the segmented region in order to individuate radiomic features that can be useful for the prognosis of PTE. Out of 919 extracted radiomic features, we found that 109 present different distributions according to the Mann–Whitney U test with corrected p-values less than 0.01. Lastly, nine uncorrelated features were retained that can be exploited to realize a prognostic signature.

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

Cited by 31 publications

References 40 publications

A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net

A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net

Unsupervised Domain Adaptation for Vertebrae Detection and Identification in 3D CT Volumes Using a Domain Sanity Loss

Lung Segmentation and Characterization in COVID-19 Patients for Assessing Pulmonary Thromboembolism: An Approach Based on Deep Learning and Radiomics

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