try3

Author: mstaring

_bibliography/papers.bib

@@ -3,6 +3,59 @@
 
 @string{aps = {American Physical Society,}}
 
+
+@article{Jia:2023,
+  abbr =     {},
+  bibtex_show = {true},
+  author =   {Jia, Jingnan and Marges, Emiel R. and Ninaber, Maarten K. and Kroft, Lucia J.M. and Schouffoer, Anne A. and Staring, Marius and Stoel, Berend C.},
+  title =    {Automatic pulmonary function estimation from chest CT scans using deep regression neural networks: the relation between structure and function in systemic sclerosis},
+  journal =  {IEEE Access},
+  volume =   {11},
+  pages =    {135272 -- 135282},
+  month =    {November},
+  year =     {2023},
+  pdf =      {2023_j_Access.pdf},
+  html =     {https://doi.org/10.1109/ACCESS.2023.3337639},
+  arxiv =    {},
+  code =     {},
+  abstract = {Pulmonary function test (PFT) plays an important role in screening and following-up pulmonary involvement in systemic sclerosis (SSc). However, some patients are not able to perform PFT due to contraindications. In addition, it is unclear how lung function is affected by changes in lung structure in SSc. Therefore, this study aims to explore the potential of automatically estimating PFT results from chest CT scans of SSc patients and how different regions influence the estimation of PFT values. Deep regression networks were developed with transfer learning to estimate PFT from 316 SSc patients. Segmented lungs and vessels were used to mask the CT images to train the network with different inputs: from entire CT scan, lungs-only to vessels-only. The network trained by entire CT scans with transfer learning achieved an ICC of 0.71, 0.76, 0.80, and 0.81 for the estimation of DLCO, FEV1, FVC and TLC, respectively. The performance of the networks gradually decreased when trained on data from lungs-only and vessels-only. Regression attention maps showed that regions close to large vessels are highlighted more than other regions, and occasionally regions outside the lungs are highlighted. These experiments mean that apart from lungs and large vessels, other regions contribute to the estimation of PFTs. In addition, adding manually designed biomarkers increased the correlation (R) from 0.75, 0.74, 0.82, and 0.83 to 0.81, 0.83, 0.88, and 0.90, respectively. It means that that manually designed imaging biomarkers can still contribute to explaining the relation between lung function and structure.},
+}
+
+@article{Neve:2023,
+  abbr =     {},
+  bibtex_show = {true},
+  author 	=  {Neve, Olaf M. and Romeijn, Stephan R. and Chen, Yunjie and Nagtegaal, Larissa and Grootjans, Willem and Jansen, Jeroen C. and Staring, Marius and Verbist, Berit M. and Hensen, Erik F.},
+  title =    {Automated 2-dimensional measurement of vestibular schwannoma: validity and accuracy of an artificial intelligence algorithm},
+  journal =  {Otolaryngology - Head and Neck Surgery},
+  volume =   {169},
+  number =   {6},
+  pages =    {1582 -- 1589},
+  month =    {December},
+  year =     {2023},
+  pdf =      {2023_j_OHNS.pdf},
+  html =     {https://doi.org/10.1002/ohn.470},
+  arxiv =    {},
+  code =     {},
+  abstract = {<b>Objective.</b> Validation of automated 2-dimensional (2D) diameter measurements of vestibular schwannomas on magnetic resonance imaging (MRI).<br><b>Study Design.</b>Retrospective validation study using 2 data sets containing MRIs of vestibular schwannoma patients.<br><b>Setting.</b> University Hospital in The Netherlands.<br><b>Methods.</b>Two data sets were used, 1 containing 1 scan per patient (n = 134) and the other containing at least 3 consecutive MRIs of 51 patients, all with contrast-enhanced T1 or high-resolution T2 sequences. 2D measurements of the maximal extrameatal diameters in the axial plane were automatically derived from a 3D-convolutional neural network compared to manual measurements by 2 human observers. Intra- and interobserver variabilities were calculated using the intraclass correlation coefficient (ICC), agreement on tumor progression using Cohen's kappa.<br><b>Results.</b> The human intra- and interobserver variability showed a high correlation (ICC: 0.98-0.99) and limits of agreement of 1.7 to 2.1 mm. Comparing the automated to human measurements resulted in ICC of 0.98 (95% confidence interval [CI]: 0.974; 0.987) and 0.97 (95% CI: 0.968; 0.984), with limits of agreement of 2.2 and 2.1 mm for diameters parallel and perpendicular to the posterior side of the temporal bone, respectively. There was satisfactory agreement on tumor progression between automated measurements and human observers (Cohen's &kappa; = 0.77), better than the agreement between the human observers (Cohen's &kappa; = 0.74).<br><b>Conclusion.</b> Automated 2D diameter measurements and growth detection of vestibular schwannomas are at least as accurate as human 2D measurements. In clinical practice, measurements of the maximal extrameatal tumor (2D) diameters of vestibular schwannomas provide important complementary information to total tumor volume (3D) measurements. Combining both in an automated measurement algorithm facilitates clinical adoption.},
+}
+
+@article{Zhai:2023,
+  abbr =     {},
+  bibtex_show = {true},
+  author =   {Zhai, Zhiwei and Boon, Gudula J.A.M. and Staring, Marius and van Dam, Lisette F. and Kroft, Lucia J.M. and Giron, Irene Hernandez and Ninaber, Maarten K. and Bogaard, Harm Jan and Meijboom, Lilian J. and Vonk Noordegraaf, Anton and Huisman, Menno V. and Klok, Frederikus A. and Stoel, Berend C.},
+  title= {Automated Quantification of the Pulmonary Vasculature in Pulmonary Embolism and Chronic Thromboembolic Pulmonary Hypertension},
+  journal =  {Pulmonary Circulation},
+  volume =   {13},
+  number =   {2},
+  pages =    {e12223},
+  year =     {2023},
+  pdf =      {2023_j_PC.pdf},
+  html =     {https://doi.org/10.1002/pul2.12223},
+  arxiv =    {},
+  code =     {},
+  abstract = {The particular mechanical obstruction of pulmonary embolism (PE) and chronic thromboembolic pulmonary hypertension (CTEPH) may affect pulmonary arteries and veins differently. Therefore, we evaluated whether pulmonary vascular morphology and densitometry using CT pulmonary angiography (CTPA) in arteries and veins could distinguish PE from CTEPH.<br>We analyzed CTPA images from a convenience cohort of 16 PE patients, 6 CTEPH patients and 15 controls without PE or CTEPH. Pulmonary vessels were extracted with a graph-cuts method, and separated into arteries and veins using a deep-learning classification method. By analyzing the distribution of vessel radii, vascular morphology was quantified into a slope (&alpha;) and intercept (&beta;) for the entire pulmonary vascular tree, and for arteries and veins, separately. To quantify lung perfusion, the median pulmonary vascular density was calculated. As a reference, lung perfusion was also quantified by the contrast enhancement in the parenchymal areas, pulmonary trunk and descending aorta. All quantifications were compared between the three groups.<br>Vascular morphology did not differ between groups, in contrast to vascular density values (both arterial and venous; p-values 0.006 - 0.014). The median vascular density (interquartile range) was -452 (95), -567 (113) and -470 (323) HU, for the PE, control and CTEPH group, respectively. The perfusion curves from all measurements showed different patterns between groups.<br>In this proof of concept study, not vasculature morphology but vascular densities differentiated between normal and thrombotic obstructed vasculature. For distinction on an individual patient level, further technical improvements are needed both in terms of image acquisition/reconstruction and post-processing.},
+}
+
 @article{Goedmakers:2022,
   abbr =     {},
   bibtex_show = {true},