Automatic quantitative computed tomography measurement of longitudinal lung volume loss in interstitial lung diseases

Objectives To compare the lung CT volume (CTvol) and pulmonary function tests in an interstitial lung disease (ILD) population. Then to evaluate the CTvol loss between idiopathic pulmonary fibrosis (IPF) and non-IPF and explore a prognostic value of annual CTvol loss in IPF. Methods We conducted in an expert center a retrospective study between 2005 and 2018 on consecutive patients with ILD. CTvol was measured automatically using commercial software based on a deep learning algorithm. In the first group, Spearman correlation coefficients (r) between forced vital capacity (FVC), total lung capacity (TLC), and CTvol were calculated. In a second group, annual CTvol loss was calculated using linear regression analysis and compared with the Mann–Whitney test. In a last group of IPF patients, annual CTvol loss was calculated between baseline and 1-year CTs for investigating with the Youden index a prognostic value of major adverse event at 3 years. Univariate and log-rank tests were calculated. Results In total, 560 patients (4610 CTs) were analyzed. For 1171 CTs, CTvol was correlated with FVC (r: 0.86) and TLC (r: 0.84) (p < 0.0001). In 408 patients (3332 CT), median annual CTvol loss was 155.7 mL in IPF versus 50.7 mL in non-IPF (p < 0.0001) over 5.03 years. In 73 IPF patients, a relative annual CTvol loss of 7.9% was associated with major adverse events (log-rank, p < 0.0001) in univariate analysis (p < 0.001). Conclusions Automated lung CT volume may be an alternative or a complementary biomarker to pulmonary function tests for the assessment of lung volume loss in ILD. Key Points • There is a good correlation between lung CT volume and forced vital capacity, as well as for with total lung capacity measurements (r of 0.86 and 0.84 respectively, p < 0.0001). • Median annual CT volume loss is significantly higher in patients with idiopathic pulmonary fibrosis than in patients with other fibrotic interstitial lung diseases (155.7 versus 50.7 mL, p < 0.0001). • In idiopathic pulmonary fibrosis, a relative annual CT volume loss higher than 9.4% is associated with a significantly reduced mean survival time at 2.0 years versus 2.8 years (log-rank, p < 0.0001).

Verte-Box: A Novel Convolutional Neural Network for Fully Automatic Segmentation of Vertebrae in CT Image

Due to the complex shape of the vertebrae and the background containing a lot of interference information, it is difficult to accurately segment the vertebrae from the computed tomography (CT) volume by manual segmentation. This paper proposes a convolutional neural network for vertebrae segmentation, named Verte-Box. Firstly, in order to enhance feature representation and suppress interference information, this paper places a robust attention mechanism on the central processing unit, including a channel attention module and a dual attention module. The channel attention module is used to explore and emphasize the interdependence between channel graphs of low-level features. The dual attention module is used to enhance features along the location and channel dimensions. Secondly, we design a multi-scale convolution block to the network, which can make full use of different combinations of receptive field sizes and significantly improve the network’s perception of the shape and size of the vertebrae. In addition, we connect the rough segmentation prediction maps generated by each feature in the feature box to generate the final fine prediction result. Therefore, the deep supervision network can effectively capture vertebrae information. We evaluated our method on the publicly available dataset of the CSI 2014 Vertebral Segmentation Challenge and achieved a mean Dice similarity coefficient of 92.18 ± 0.45%, an intersection over union of 87.29 ± 0.58%, and a 95% Hausdorff distance of 7.7107 ± 0.5958, outperforming other algorithms.

Prostate deformation during hypofractionated radiotherapy: an analysis of implanted fiducial marker displacement

Background To report prostate deformation during treatment, based on an analysis of fiducial marker positional differences in a large sample. Material and methods This study included 144 patients treated with prostate stereotactic body radiation therapy after implantation in each of 4 gold fiducial markers (FMs), which were located and numbered consistently. The center of mass of the FMs was recorded for every pair of X-ray images taken during treatment. The distance between each pair of fiducials in the live X-ray images is calculated and compared with the respective distances as determined in the CT volume. The RBE is the difference between these distances. Mean RBE and intrafraction and interfraction RBE were evaluated. The intrafraction and intefraction RBE variability were defined as the standard deviation, respectively, of all RBE during 1 treatment fraction and of the mean daily RBE over the whole treatment course. Results We analyzed 720 treatment fractions comprising 24,453 orthogonal X-ray image acquisitions. We observed a trend to higher RBE related to FM4 (apex) during treatment. The fiducial marker in the prostate apex could not be used in 16% of observations, in which RBE was > 2.5 mm. The mean RBEavg was 0.93 ± 0.39 mm (range 0.32–1.79 mm) over the 5 fractions. The RBEavg was significantly lower for the first and second fraction compared with the others (P < .001). The interfraction variability of RBEavg was 0.26 ± 0.16 mm (range 0.04–0.74 mm). The mean intrafraction variability of all FMs was 0.45 ± 0.25 mm. The highest Pearson correlation coefficient was observed between FM2 and FM3 (middle left and right prostate) (R = 0.78; P < .001). Every combination with FM4 yielded lower coefficients (range 0.66–0.71; P < .001), indicating different deformation of the prostate apex. Conclusions Ideally, prostate deformation is generally small, but it is very sensitive to rectal and bladder filling. We observed RBE up to 11.3 mm. The overall correlation between FMs was affected by shifts of individual fiducials, indicating that the prostate is not a “rigid” organ. Systematic change of RBE average between subsequent fractions indicates a systematic change in prostate shape.

Application of Multislice Spiral CT Volume Reconstruction in Congenital Microtia Reconstruction

Objective. To investigate the application of multislice spiral CT in volume reconstruction of congenital microtia. Methods. Sixty patients who underwent auricle reconstruction in Otolaryngology Hospital of our hospital from April 2020 to April 2021 were selected. All patients had no mental disorders and normal cognitive ability and volunteered to participate in this study. Multislice spiral CT was used to evaluate the diagnostic accuracy of multislice spiral CT by scanning the morphology of the ossicular chain and the bone destruction of the selected patients. Results. MSCT can clearly display the structure of ear. Conclusion. MSCT can clearly reflect the external ear and the structure of the ear in patients with congenital microtia and distinguish the different types of patients obviously. Multiplane reconstruction and volume reconstruction can clearly display the fine structure of the patient’s ear, which has important reference value for surgery.

DALU-Net: Automated Liver Segmentation and Volumetry for Liver Transplantation in Abdominal Computed Tomography Volumes

Liver transplantation is performed in patients with liver disease, using the liver of a braindead or living donor. In living-donor liver transplantation, the safety of the donor is critical. In addition, the amount that can be resected from the living donor is limited. It is important that accurately measuring the liver volume to avoid graft size mismatch. In this paper, we designed a deep attention convolutional long short-term memory (CLSTM) network architecture for liver segmentation that combines an attention mechanism, deep supervision, and CLSTM. The proposed model can focus on the liver in abdominal CT volume data and can learn inter-slice using CLSTM. Our framework was trained using 133 training cases, 29 validation cases, and 29 test cases of liver donors. We compared livers and volumes manually labeled by a liver transplant surgeon and those obtained by automatic segmentation of livers and volumes. We further evaluated the segmentation and volumetry of the left lobe, right lobe, and caudate lobe, according to the anatomical structure of the liver. Our approach significantly outperformed the 3D U-Net in terms of accuracy. Our approach can be used as an aid in estimating liver volume from CT volume data for living-donor liver transplantation.

Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT

The Coronavirus disease 2019 (COVID-19) presents open questions in how we clinically diagnose and assess disease course. Recently, chest computed tomography (CT) has shown utility for COVID-19 diagnosis. In this study, we developed Deep COVID DeteCT (DCD), a deep learning convolutional neural network (CNN) that uses the entire chest CT volume to automatically predict COVID-19 (COVID+) from non-COVID-19 (COVID−) pneumonia and normal controls. We discuss training strategies and differences in performance across 13 international institutions and 8 countries. The inclusion of non-China sites in training significantly improved classification performance with area under the curve (AUCs) and accuracies above 0.8 on most test sites. Furthermore, using available follow-up scans, we investigate methods to track patient disease course and predict prognosis.