Deep Negative Volume Segmentation

06/22/2020
by   Kristina Belikova, et al.
0

Clinical examination of three-dimensional image data of compound anatomical objects, such as complex joints, remains a tedious process, demanding the time and the expertise of physicians. For instance, automation of the segmentation task of the TMJ (temporomandibular joint) has been hindered by its compound three-dimensional shape, multiple overlaid textures, an abundance of surrounding irregularities in the skull, and a virtually omnidirectional range of the jaw's motion - all of which extend the manual annotation process to more than an hour per patient. To address the challenge, we invent a new angle to the 3D segmentation task: namely, we propose to segment empty spaces between all the tissues surrounding the object - the so-called negative volume segmentation. Our approach is an end-to-end pipeline that comprises a V-Net for bone segmentation, a 3D volume construction by inflation of the reconstructed bone head in all directions along the normal vector to its mesh faces. Eventually confined within the skull bones, the inflated surface occupies the entire "negative" space in the joint, effectively providing a geometrical/topological metric of the joint's health. We validate the idea on the CT scans in a 50-patient dataset, annotated by experts in maxillofacial medicine, quantitatively compare the asymmetry given the left and the right negative volumes, and automate the entire framework for clinical adoption.

READ FULL TEXT

page 1

page 2

page 4

page 6

page 7

page 9

page 18

page 19

research
08/15/2018

AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy

Methods: Our deep learning model, called AnatomyNet, segments OARs from ...
research
11/13/2019

Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection

Multiple instance learning (MIL) is a supervised learning methodology th...
research
06/18/2018

Kid-Net: Convolution Networks for Kidney Vessels Segmentation from CT-Volumes

Semantic image segmentation plays an important role in modeling patient-...
research
08/18/2020

PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data

The 3D volumetric shape of the heart's left ventricle (LV) myocardium (M...
research
08/15/2018

AnatomyNet: Deep 3D Squeeze-and-excitation U-Nets for fast and fully automated whole-volume anatomical segmentation

Radiation therapy (RT) is a common treatment for head and neck (HaN) can...
research
02/11/2021

Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding

As lung cancer evolves, the presence of enlarged and potentially maligna...
research
12/09/2018

More Knowledge is Better: Cross-Modality Volume Completion and 3D+2D Segmentation for Intracardiac Echocardiography Contouring

Using catheter ablation to treat atrial fibrillation increasingly relies...

Please sign up or login with your details

Forgot password? Click here to reset