TY - UNPB

T1 - Deep Negative Volume Segmentation

AU - Рыбаков, Александр

AU - Дылов, Дмитрий

AU - Маслов, Максим

AU - Беликова, Кристина

AU - Рогов, Олег

PY - 2020/6/22

Y1 - 2020/6/22

N2 - 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.

AB - 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.

M3 - Working paper

T3 - arXiv

BT - Deep Negative Volume Segmentation

ER -