Towards Unifying Anatomy Segmentation: Automated Generation of a
Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines
- URL: http://arxiv.org/abs/2307.13375v1
- Date: Tue, 25 Jul 2023 09:48:13 GMT
- Title: Towards Unifying Anatomy Segmentation: Automated Generation of a
Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines
- Authors: Alexander Jaus, Constantin Seibold, Kelsey Hermann, Alexandra Walter,
Kristina Giske, Johannes Haubold, Jens Kleesiek, Rainer Stiefelhagen
- Abstract summary: We generate a dataset of whole-body CT scans with $142$ voxel-level labels for 533 volumes providing comprehensive anatomical coverage.
Our proposed procedure does not rely on manual annotation during the label aggregation stage.
We release our trained unified anatomical segmentation model capable of predicting $142$ anatomical structures on CT data.
- Score: 113.08940153125616
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this study, we present a method for generating automated anatomy
segmentation datasets using a sequential process that involves nnU-Net-based
pseudo-labeling and anatomy-guided pseudo-label refinement. By combining
various fragmented knowledge bases, we generate a dataset of whole-body CT
scans with $142$ voxel-level labels for 533 volumes providing comprehensive
anatomical coverage which experts have approved. Our proposed procedure does
not rely on manual annotation during the label aggregation stage. We examine
its plausibility and usefulness using three complementary checks: Human expert
evaluation which approved the dataset, a Deep Learning usefulness benchmark on
the BTCV dataset in which we achieve 85% dice score without using its training
dataset, and medical validity checks. This evaluation procedure combines
scalable automated checks with labor-intensive high-quality expert checks.
Besides the dataset, we release our trained unified anatomical segmentation
model capable of predicting $142$ anatomical structures on CT data.
Related papers
- A Dataset for Deep Learning-based Bone Structure Analyses in Total Hip
Arthroplasty [8.604089365903029]
Total hip anatomy (THA) is a widely used surgical procedure in orthopedics.
Deep learning technologies are promising but require high-quality labeled data for the learning.
We propose an efficient data annotation pipeline for producing a deep learning-oriented dataset.
arXiv Detail & Related papers (2023-06-07T16:28:53Z) - Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via
Volumetric Pseudo-Labeling [66.75096111651062]
We created a large-scale dataset of 10,021 thoracic CTs with 157 labels.
We applied an ensemble of 3D anatomy segmentation models to extract anatomical pseudo-labels.
Our resulting segmentation models demonstrated remarkable performance on CXR.
arXiv Detail & Related papers (2023-06-06T18:01:08Z) - Clinically Acceptable Segmentation of Organs at Risk in Cervical Cancer
Radiation Treatment from Clinically Available Annotations [0.0]
We present an approach to learn a deep learning model for the automatic segmentation of Organs at Risk (OARs) in cervical cancer radiation treatment.
We employ simples for automatic data cleaning to minimize data inhomogeneity, label noise, and missing annotations.
We develop a semi-supervised learning approach utilizing a teacher-student setup, annotation imputation, and uncertainty-guided training to learn in presence of missing annotations.
arXiv Detail & Related papers (2023-02-21T13:24:40Z) - Ensemble uncertainty as a criterion for dataset expansion in distinct
bone segmentation from upper-body CT images [0.7388859384645263]
The localisation and segmentation of individual bones is an important preprocessing step in many planning and navigation applications.
We present an end-to-end learnt algorithm that is capable of segmenting 125 distinct bones in an upper-body CT.
We also provide an ensemble-based uncertainty measure that helps to single out scans to enlarge the training dataset with.
arXiv Detail & Related papers (2022-08-19T08:39:23Z) - TotalSegmentator: robust segmentation of 104 anatomical structures in CT
images [48.50994220135258]
We present a deep learning segmentation model for body CT images.
The model can segment 104 anatomical structures relevant for use cases such as organ volumetry, disease characterization, and surgical or radiotherapy planning.
arXiv Detail & Related papers (2022-08-11T15:16:40Z) - Deeply supervised UNet for semantic segmentation to assist
dermatopathological assessment of Basal Cell Carcinoma (BCC) [2.031570465477242]
We focus on detecting Basal Cell Carcinoma (BCC) through semantic segmentation using several models based on the UNet architecture.
We analyze two different encoders for the first part of the UNet network and two additional training strategies.
The best model achieves over 96%, accuracy, sensitivity, and specificity on the test set.
arXiv Detail & Related papers (2021-03-05T15:39:55Z) - Chest x-ray automated triage: a semiologic approach designed for
clinical implementation, exploiting different types of labels through a
combination of four Deep Learning architectures [83.48996461770017]
This work presents a Deep Learning method based on the late fusion of different convolutional architectures.
We built four training datasets combining images from public chest x-ray datasets and our institutional archive.
We trained four different Deep Learning architectures and combined their outputs with a late fusion strategy, obtaining a unified tool.
arXiv Detail & Related papers (2020-12-23T14:38:35Z) - An Extensive Study on Cross-Dataset Bias and Evaluation Metrics
Interpretation for Machine Learning applied to Gastrointestinal Tract
Abnormality Classification [2.985964157078619]
Automatic analysis of diseases in the GI tract is a hot topic in computer science and medical-related journals.
A clear understanding of evaluation metrics and machine learning models with cross datasets is crucial to bring research in the field to a new quality level.
We present comprehensive evaluations of five distinct machine learning models that can classify 16 different GI tract conditions.
arXiv Detail & Related papers (2020-05-08T08:59:31Z) - Self-Training with Improved Regularization for Sample-Efficient Chest
X-Ray Classification [80.00316465793702]
We present a deep learning framework that enables robust modeling in challenging scenarios.
Our results show that using 85% lesser labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
arXiv Detail & Related papers (2020-05-03T02:36:00Z) - VerSe: A Vertebrae Labelling and Segmentation Benchmark for
Multi-detector CT Images [121.31355003451152]
Large Scale Vertebrae Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020.
We present the the results of this evaluation and further investigate the performance-variation at vertebra-level, scan-level, and at different fields-of-view.
arXiv Detail & Related papers (2020-01-24T21:09:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.