Dataset Information

A robust method for automatic identification of landmarks on surface models of the pelvis.

ABSTRACT: The recognition of bony landmarks of the pelvis is a required operation in patient-specific orthopedics, subject-specific biomechanics or morphometrics. A fully automatic detection is preferable to a subjective and time-consuming manual identification. In this paper, a new approach, called the iterative tangential plane (ITP) method, for fully automatic identification of landmarks on surface models of the pelvis is introduced. The method includes the landmarks to construct the two most established anatomical reference frames of the pelvis: the anterior pelvic plane (APP) coordinate system and superior inferior spine plane (SISP) coordinate system. The ITP method proved to be robust against the initial alignment of the pelvis in space. A comparison to a manual identification was performed that showed minor but significant (p?

SUBMITTER: Fischer MCM

PROVIDER: S-EPMC6746744 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A robust method for automatic identification of landmarks on surface models of the pelvis.

Fischer Maximilian C M MCM Krooß Felix F Habor Juliana J Radermacher Klaus K

Scientific reports 20190916 1

The recognition of bony landmarks of the pelvis is a required operation in patient-specific orthopedics, subject-specific biomechanics or morphometrics. A fully automatic detection is preferable to a subjective and time-consuming manual identification. In this paper, a new approach, called the iterative tangential plane (ITP) method, for fully automatic identification of landmarks on surface models of the pelvis is introduced. The method includes the landmarks to construct the two most establish ...[more]

PMID: 31527599

Similar Datasets

Project description:PurposeMinimally invasive alternatives are now available for many complex surgeries. These approaches are enabled by the increasing availability of intra-operative image guidance. Yet, fluoroscopic X-rays suffer from projective transformation and thus cannot provide direct views onto anatomy. Surgeons could highly benefit from additional information, such as the anatomical landmark locations in the projections, to support intra-operative decision making. However, detecting landmarks is challenging since the viewing direction changes substantially between views leading to varying appearance of the same landmark. Therefore, and to the best of our knowledge, view-independent anatomical landmark detection has not been investigated yet.MethodsIn this work, we propose a novel approach to detect multiple anatomical landmarks in X-ray images from arbitrary viewing directions. To this end, a sequential prediction framework based on convolutional neural networks is employed to simultaneously regress all landmark locations. For training, synthetic X-rays are generated with a physically accurate forward model that allows direct application of the trained model to real X-ray images of the pelvis. View invariance is achieved via data augmentation by sampling viewing angles on a spherical segment of [Formula: see text].ResultsOn synthetic data, a mean prediction error of 5.6 ± 4.5 mm is achieved. Further, we demonstrate that the trained model can be directly applied to real X-rays and show that these detections define correspondences to a respective CT volume, which allows for analytic estimation of the 11 degree of freedom projective mapping.ConclusionWe present the first tool to detect anatomical landmarks in X-ray images independent of their viewing direction. Access to this information during surgery may benefit decision making and constitutes a first step toward global initialization of 2D/3D registration without the need of calibration. As such, the proposed concept has a strong prospect to facilitate and enhance applications and methods in the realm of image-guided surgery.

Dataset Information

A robust method for automatic identification of landmarks on surface models of the pelvis.

Publications

A robust method for automatic identification of landmarks on surface models of the pelvis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure