Deep Negative Volume Segmentation

Abstract

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.

20 pages, 5 main figures, 3 tables, 11 supplemental figures, supplementary material

References in corpus (8)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Attention U-Net: Learning Where to Look for the Pancreas
• A review: Deep learning for medical image segmentation using multi-modality fusion
• UNet++: A Nested U-Net Architecture for Medical Image Segmentation
• MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation
• 3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation
• Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach
• FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images

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