Relational Reasoning Network (RRN) for Anatomical Landmarking

04/08/2019
by   Neslisah Torosdagli, et al.
14

Accurately identifying anatomical landmarks is a crucial step in deformation analysis and surgical planning for craniomaxillofacial (CMF) bones. Available methods require segmentation of the object of interest for precise landmarking. Unlike those, our purpose in this study is to perform anatomical landmarking using the inherent relation of CMF bones without explicitly segmenting them. We propose a new deep network architecture, called relational reasoning network (RRN), to accurately learn the local and the global relations of the landmarks. Specifically, we are interested in learning landmarks in CMF region: mandible, maxilla, and nasal bones. The proposed RRN works in an end-to-end manner, utilizing learned relations of the landmarks based on dense-block units and without the need for segmentation. For a given a few landmarks as input, the proposed system accurately and efficiently localizes the remaining landmarks on the aforementioned bones. For a comprehensive evaluation of RRN, we used cone-beam computed tomography (CBCT) scans of 250 patients. The proposed system identifies the landmark locations very accurately even when there are severe pathologies or deformations in the bones. The proposed RRN has also revealed unique relationships among the landmarks that help us infer several reasoning about informativeness of the landmark points. RRN is invariant to order of landmarks and it allowed us to discover the optimal configurations (number and location) for landmarks to be localized within the object of interest (mandible) or nearby objects (maxilla and nasal). To the best of our knowledge, this is the first of its kind algorithm finding anatomical relations of the objects using deep learning.

READ FULL TEXT

page 1

page 2

page 5

page 7

page 8

research
10/06/2018

Deep Geodesic Learning for Segmentation and Anatomical Landmarking

In this paper, we propose a novel deep learning framework for anatomy se...
research
03/08/2021

You Only Learn Once: Universal Anatomical Landmark Detection

Detecting anatomical landmarks in medical images plays an essential role...
research
10/17/2011

Algorithms to automatically quantify the geometric similarity of anatomical surfaces

We describe new approaches for distances between pairs of 2-dimensional ...
research
06/12/2015

A Novel Hybrid Approach for Cephalometric Landmark Detection

Cephalometric analysis has an important role in dentistry and especially...
research
03/11/2018

Calculating the Midsagittal Plane for Symmetrical Bilateral Shapes: Applications to Clinical Facial Surgical Planning

It is difficult to estimate the midsagittal plane of human subjects with...
research
05/18/2023

Enhancing Speech Articulation Analysis using a Geometric Transformation of the X-ray Microbeam Dataset

Accurate analysis of speech articulation is crucial for speech analysis....
research
09/24/2021

Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans

Accurately segmenting teeth and identifying the corresponding anatomical...

Please sign up or login with your details

Forgot password? Click here to reset