@inproceedings{Noothout2018b,
title = {CNN-based Landmark Detection in Cardiac CTA Scans},
author = {J.M.H. Noothout, B.D de Vos, J.M. Wolterink, T. Leiner, I. Isgum},
url = {https://openreview.net/forum?id=r1malb3jz},
year = {2018},
date = {2018-05-20},
booktitle = {Medical Imaging with Deep Learning. MIDL Amsterdam},
abstract = {Fast and accurate anatomical landmark detection can benefit many medical image analysis methods. Here, we propose a method to automatically detect anatomical landmarks in medical images. Automatic landmark detection is performed with a patch-based fully convolutional neural network (FCNN) that combines regression and classification. For any given image patch, regression is used to predict the 3D displacement vector from the image patch to the landmark. Simultaneously, classification is used to identify patches that contain the landmark. Under the assumption that patches close to a landmark can determine the landmark location more precisely than patches further from it, only those patches that contain the landmark according to classification are used to determine the landmark location. The landmark location is obtained by calculating the average landmark location using the computed 3D displacement vectors. The method is evaluated using detection of six clinically relevant landmarks in coronary CT angiography (CCTA) scans : the right and left ostium, the bifurcation of the left main coronary artery (LM) into the left anterior descending and the left circumflex artery, and the origin of the right, non-coronary, and left aortic valve commissure. The proposed method achieved an average Euclidean distance error of 2.19 mm and 2.88 mm for the right and left ostium respectively, 3.78 mm for the bifurcation of the LM, and 1.82 mm, 2.10 mm and 1.89 mm for the origin of the right, non-coronary, and left aortic valve commissure respectively, demonstrating accurate performance. The proposed combination of regression and classification can be used to accurately detect landmarks in CCTA scans.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@conference{Noothout2019,
title = {Deep Learning for Automatic Landmark Localization in CTA for Transcatheter Aortic Valve Implantation},
author = {Julia M.H. Noothout, Bob D. de Vos, J.M. Wolterink, Richard A.P. Takx, Tim Leiner, Ivana Išgum},
url = {http://dlmedia.eu/landmarks_rsna2019_final-3/},
year = {2019},
date = {2019-12-03},
booktitle = {Radiological Society of North America, 105th Annual Meeting},
abstract = {PURPOSE
Fast and accurate automatic landmark localization in CT angiography (CTA) scans can aid treatment planning for patients undergoing transcatheter aortic valve implantation (TAVI). Manual localization of landmarks can be time-consuming and cumbersome. Automatic landmark localization can potentially reduce post-processing time and interobserver variability. Hence, this study evaluates the performance of deep learning for automatic aortic root landmark localization in CTA.
METHOD AND MATERIALS
This study included 672 retrospectively gated CTA scans acquired as part of clinical routine (Philips Brilliance iCT-256 scanner, 0.9mm slice thickness, 0.45mm increment, 80-140kVp, 210-300mAs, contrast). Reference standard was defined by manual localization of the left (LH), non-coronary (NCH) and right (RH) aortic valve hinge points, and the right (RO) and left (LO) coronary ostia. To develop and evaluate the automatic method, 412 training, 60 validation, and 200 test CTAs were randomly selected. 100/200 test CTAs were annotated twice by the same observer and once by a second observer to estimate intra- and interobserver agreement. Five CNNs with identical architectures were trained, one for the localization of each landmark. For treatment planning of TAVI, distances between landmark points are used, hence performance was evaluated on subvoxel level with the Euclidean distance between reference and automatically predicted landmark locations.
RESULTS
Median (IQR) distance errors for the LH, NCH and RH were 2.44 (1.79), 3.01 (1.82) and 2.98 (2.09)mm, respectively. Repeated annotation of the first observer led to distance errors of 2.06 (1.43), 2.57 (2.22) and 2.58 (2.30)mm, and for the second observer to 1.80 (1.32), 1.99 (1.28) and 1.81 (1.68)mm, respectively. Median (IQR) distance errors for the RO and LO were 1.65 (1.33) and 1.91 (1.58)mm, respectively. Repeated annotation of the first observer led to distance errors of 1.43 (1.05) and 1.92 (1.44)mm, and for the second observer to 1.78 (1.55) and 2.35 (1.56)mm, respectively. On average, analysis took 0.3s/CTA.
CONCLUSION
Automatic landmark localization in CTA approaches second observer performance and thus enables automatic, accurate and reproducible landmark localization without additional reading time.
CLINICAL RELEVANCE/APPLICATION
Automatic landmark localization in CTA can aid in reducing post-processing time and interobserver variability in treatment planning for patients undergoing TAVI.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}