CT Segmentation of Dinosaur Fossils by Deep Learning

Congyu Yu,Mark Norell,Fangbo Qin,Ying Li,Zichuan Qin

doi:10.3389/feart.2021.805271

Abstract

Recently, deep learning has reached significant advancements in various image-related tasks, particularly in medical sciences. Deep neural networks have been used to facilitate diagnosing medical images generated from various observation techniques including CT (computed tomography) scans. As a non-destructive 3D imaging technique, CT scan has also been widely used in paleontological research, which provides the solid foundation for taxon identification, comparative anatomy, functional morphology, etc. However, the labeling and segmentation of CT images are often laborious, prone to error, and subject to researchers own judgements. It is essential to set a benchmark in CT imaging processing of fossils and reduce the time cost from manual processing. Since fossils from the same localities usually share similar sedimentary environments, we constructed a dataset comprising CT slices of protoceratopsian dinosaurs from the Gobi Desert, Mongolia. Here we tested the fossil segmentation performances of U-net, a classic deep neural network for image segmentation, and constructed a modified DeepLab v3+ network, which included MobileNet v1 as feature extractor and practiced an atrous convolutional method that can capture features from various scales. The results show that deep neural network can efficiently segment protoceratopsian dinosaur fossils, which can save significant time from current manual segmentation. But further test on a dataset generated by other vertebrate fossils, even from similar localities, is largely limited.

Highlights

Vertebrate paleontology is based on fossils that are remains of ancient organisms
The results show that deep neural network can efficiently segment protoceratopsian dinosaur fossils, which can save significant time from current manual segmentation
CT scans have been successfully applied to various branches in vertebrate paleontology (Figures 1A–E), for example the studies of archosaur brain endocasts (Watanabe et al, 2019), Fossil Segmentation by Deep Learning mammalian inner ears (Luo and Ketten 1991), theropod dinosaur body mass estimation (Allen et al, 2013), finite element analysis of the Allosaurus skull (Rayfield et al, 2001), and horned dinosaur tooth replacement patterns (He et al, 2018)

Summary

Introduction

Vertebrate paleontology is based on fossils that are remains of ancient organisms. Because fossils usually do not preserve molecular or behavior information, paleontologists mainly focus on their morphology, which includes exterior features and interior structures like brain endocasts and inner ears. CT scan and other non-destructive imaging techniques have greatly facilitated the development of vertebrate paleontology in revealing hidden structures and providing 3D models for teaching and exhibition. CT scans have been successfully applied to various branches in vertebrate paleontology (Figures 1A–E), for example the studies of archosaur brain endocasts (Watanabe et al, 2019), Fossil Segmentation by Deep Learning mammalian inner ears (Luo and Ketten 1991), theropod dinosaur body mass estimation (Allen et al, 2013), finite element analysis of the Allosaurus skull (Rayfield et al, 2001), and horned dinosaur tooth replacement patterns (He et al, 2018). For fragile specimens that cannot be mechanically prepared, CT scan (or other non-invasive scanning techniques) is the only way to make detailed observations. Such scanners are common elements in many paleontological laboratories. Processing the data generated by CT and other observational equipment is both arduous and time-consuming

Methods

Results

Discussion

Conclusion