Efficient ribcage segmentation from CT scans using shape features

A Decision-Support Informatics Platform for Minimally Invasive Aortic Valve Replacement

Minimally invasive aortic valve replacement is performed by mini-sternotomy (MS) or less invasive right anterior mini-thoracotomy (RT). The possibility of adopting RT is assessed by anatomical criteria derived from manual 2D image analysis. We developed a semi-automatic tool (RT-PLAN) to assess the criteria of RT, extract other parameters of surgical interest and generate a view of the anatomical region in a 3D space. Twenty-five 3D CT images from a dataset were retrospectively evaluated. The methodology starts with segmentation to reconstruct 3D surface models of the aorta and anterior rib cage. Secondly, the RT criteria and geometric information from these models are automatically and quantitatively evaluated. A comparison is made between the values of the parameters measured by the standard manual 2D procedure and our tool. The RT-PLAN procedure was feasible in all cases. Strong agreement was found between RT-PLAN and the standard manual 2D procedure. There was no difference between the RT-PLAN and the standard procedure when selecting patients for the RT technique. The tool developed is able to effectively perform the assessment of the RT criteria, with the addition of a realistic visualisation of the surgical field through virtual reality technology.

3D Segmentation Algorithms for Computerized Tomographic Imaging: a Systematic Literature Review

This paper presents a systematic literature review concerning 3D segmentation algorithms for computerized tomographic imaging. This analysis covers articles published in the range 2006-March 2018 found in four scientific databases (Science Direct, IEEEXplore, ACM, and PubMed), using the methodology for systematic review proposed by Kitchenham. We present the analyzed segmentation methods categorized according to its application, algorithmic strategy, validation, and use of prior knowledge, as well as its general conceptual description. Additionally, we present a general overview, discussions, and further prospects for the 3D segmentation methods applied for tomographic images.

Osteoblastic lesion screening with an advanced post-processing package enabling in-plane rib reading in CT-images

Background

To evaluate screening and diagnostic accuracy for the detection of osteoblastic rib lesions using an advanced post-processing package enabling in-plane rib reading in CT-images.

Methods

We retrospectively assessed the CT-data of 60 consecutive prostate cancer patients by applying dedicated software enabling in-plane rib reading. Reading the conventional multiplanar reconstructions was considered to be the reference standard. To simulate clinical practice, the reader was given 10 s to screen for sclerotic rib lesions in each patient applying both approaches. Afterwards, every rib was evaluated individually with both approaches without a time limit. Sensitivities, specificities, positive/negative predictive values and the time needed for detection were calculated depending on the lesion’s size (largest diameter < 5 mm, 5–10 mm, > 10 mm).

Results

In 53 of 60 patients, all ribs were properly displayed in plane, in five patients ribs were partially displayed correctly, and in two patients none of the ribs were displayed correctly. During the 10-s screening approach all patients with sclerotic rib lesions were correctly identified reading the in-plane images (including the patients without a correct rib segmentation), whereas 14 of 23 patients were correctly identified reading conventional multiplanar images. Overall screening sensitivity, specificity, and positive/negative predictive values were 100/27.0/46.0/100 %, respectively, for in-plane reading and 60.9/100/100/80.4 %, respectively, for multiplanar reading. Overall diagnostic (no time limit) sensitivity, specificity, and positive/negative predictive values of in-plane reading were 97.8/92.8/74.6/99.5 %, respectively. False positive results predominantly occurred for lesions <5 mm in size.

Conclusions

In-plane reading of the ribs allows reliable detection of osteoblastic lesions for screening purposes. The limited specificity results from false positives predominantly occurring for small lesions.

Atlas-based rib-bone detection in chest X-rays

This paper investigates using rib-bone atlases for automatic detection of rib-bones in chest X-rays (CXRs). We built a system that takes patient X-ray and model atlases as input and automatically computes the posterior rib borders with high accuracy and efficiency. In addition to conventional atlas, we propose two alternative atlases: (i) automatically computed rib bone models using Computed Tomography (CT) scans, and (ii) dual energy CXRs. We test the proposed approach with each model on 25 CXRs from the Japanese Society of Radiological Technology (JSRT) dataset and another 25 CXRs from the National Library of Medicine CXR dataset. We achieve an area under the ROC curve (AUC) of about 95% for Montgomery and 91% for JSRT datasets. Using the optimal operating point of the ROC curve, we achieve a segmentation accuracy of 88.91±1.8% for Montgomery and 85.48±3.3% for JSRT datasets. Our method produces comparable results with the state-of-the-art algorithms. The performance of our method is also excellent on challenging X-rays as it successfully addressed the rib-shape variance between patients and number of visible rib-bones due to patient respiration.

Computer-aided pulmonary image analysis in small animal models

PURPOSE

To develop an automated pulmonary image analysis framework for infectious lung diseases in small animal models.

METHODS

The authors describe a novel pathological lung and airway segmentation method for small animals. The proposed framework includes identification of abnormal imaging patterns pertaining to infectious lung diseases. First, the authors' system estimates an expected lung volume by utilizing a regression function between total lung capacity and approximated rib cage volume. A significant difference between the expected lung volume and the initial lung segmentation indicates the presence of severe pathology, and invokes a machine learning based abnormal imaging pattern detection system next. The final stage of the proposed framework is the automatic extraction of airway tree for which new affinity relationships within the fuzzy connectedness image segmentation framework are proposed by combining Hessian and gray-scale morphological reconstruction filters.

RESULTS

133 CT scans were collected from four different studies encompassing a wide spectrum of pulmonary abnormalities pertaining to two commonly used small animal models (ferret and rabbit). Sensitivity and specificity were greater than 90% for pathological lung segmentation (average dice similarity coefficient > 0.9). While qualitative visual assessments of airway tree extraction were performed by the participating expert radiologists, for quantitative evaluation the authors validated the proposed airway extraction method by using publicly available EXACT'09 data set.

CONCLUSIONS

The authors developed a comprehensive computer-aided pulmonary image analysis framework for preclinical research applications. The proposed framework consists of automatic pathological lung segmentation and accurate airway tree extraction. The framework has high sensitivity and specificity; therefore, it can contribute advances in preclinical research in pulmonary diseases.

A generic approach to pathological lung segmentation

In this study, we propose a novel pathological lung segmentation method that takes into account neighbor prior constraints and a novel pathology recognition system. Our proposed framework has two stages; during stage one, we adapted the fuzzy connectedness (FC) image segmentation algorithm to perform initial lung parenchyma extraction. In parallel, we estimate the lung volume using rib-cage information without explicitly delineating lungs. This rudimentary, but intelligent lung volume estimation system allows comparison of volume differences between rib cage and FC based lung volume measurements. Significant volume difference indicates the presence of pathology, which invokes the second stage of the proposed framework for the refinement of segmented lung. In stage two, texture-based features are utilized to detect abnormal imaging patterns (consolidations, ground glass, interstitial thickening, tree-inbud, honeycombing, nodules, and micro-nodules) that might have been missed during the first stage of the algorithm. This refinement stage is further completed by a novel neighboring anatomy-guided segmentation approach to include abnormalities with weak textures, and pleura regions. We evaluated the accuracy and efficiency of the proposed method on more than 400 CT scans with the presence of a wide spectrum of abnormalities. To our best of knowledge, this is the first study to evaluate all abnormal imaging patterns in a single segmentation framework. The quantitative results show that our pathological lung segmentation method improves on current standards because of its high sensitivity and specificity and may have considerable potential to enhance the performance of routine clinical tasks.