Proceedings ArticleDOI
Object Segmentation with Deep Neural Nets Coupled with a Shape Prior, When Learning From a Training set of Limited Quality and Small Size
Saurabh J. Shigwan,Akshay V. Gaikwad,Suyash P. Awate +2 more
- pp 1149-1153
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TLDR
This work proposes to couple deep neural networks with a pointset-based shape prior that can be learned effectively despite training sets having small size and imperfections in expert curation.Abstract:
Statistical shape priors can be crucial in segmenting objects when the data differentiates poorly between the object and its surroundings. For reliable learning, while some methods need high-quality expert segmentations, other methods need large training sets, both of which can often be difficult to obtain in clinical deployment or scientific studies. We propose to couple deep neural networks with a pointset-based shape prior that can be learned effectively despite training sets having small size and imperfections in expert curation. The prior relies on sparse Riemannian modeling in Kendall shape space. Results on clinical brain magnetic resonance imaging data show that our framework improves over the state of the art in segmenting the thalamus and the caudate.read more
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Journal ArticleDOI
Automatic quadriceps and patellae segmentation of MRI with cascaded U2-Net and SASSNet deep learning model
Ruida Cheng,Marion Crouzier,Marion Crouzier,Marion Crouzier,François Hug,Kylie Tucker,Paul Juneau,Evan S. McCreedy,William Gandler,Matthew J. McAuliffe,Frances T. Sheehan +10 more
Abstract: PURPOSE Automatic muscle segmentation is critical for advancing our understanding of human physiology, biomechanics, and musculoskeletal pathologies, as it allows for timely exploration of large multi-dimensional image sets. Segmentation models are rarely developed/validated for the pediatric model. As such, autosegmentation is not available to explore how muscle architectural changes during development and how disease/pathology affects the developing musculoskeletal system. Thus, we aimed to develop and validate an end-to-end, fully automated, deep learning model for accurate segmentation of the rectus femoris and vastus lateral, medialis, and intermedialis using a pediatric database. METHODS We developed a two-stage cascaded deep learning model in a coarse-to-fine manner. In the first stage, the U2 -Net roughly detects the muscle subcompartment region. Then, in the second stage, the shape-aware 3D semantic segmentation method SASSNet refines the cropped target regions to generate the more finer and accurate segmentation masks. We utilized multifeature image maps in both stages to stabilize performance and validated their use with an ablation study. The second-stage SASSNet was independently run and evaluated with three different cropped region resolutions: the original image resolution, and images downsampled 2× and 4× (high, mid, and low). The relationship between image resolution and segmentation accuracy was explored. In addition, the patella was included as a comparator to past work. We evaluated segmentation accuracy using leave-one-out testing on a database of 3D MR images (0.43 × 0.43 × 2 mm) from 40 pediatric participants (age 15.3 ± 1.9 years, 55.8 ± 11.8 kg, 164.2 ± 7.9 cm, 38F/2 M). RESULTS The mid-resolution second stage produced the best results for the vastus medialis, rectus femoris, and patella (Dice similarity coefficient = 95.0%, 95.1%, 93.7%), whereas the low-resolution second stage produced the best results for the vastus lateralis and vastus intermedialis (DSC = 94.5% and 93.7%). In comparing the low- to mid-resolution cases, the vasti intermedialis, vastus medialis, rectus femoris, and patella produced significant differences (p = 0.0015, p = 0.0101, p < 0.0001, p = 0.0003) and the vasti lateralis did not (p = 0.2177). The high-resolution stage 2 had significantly lower accuracy (1.0 to 4.4 dice percentage points) compared to both the mid- and low-resolution routines (p value ranged from < 0.001 to 0.04). The one exception was the rectus femoris, where there was no difference between the low- and high-resolution cases. The ablation study demonstrated that the multifeature is more reliable than the single feature. CONCLUSIONS Our successful implementation of this two-stage segmentation pipeline provides a critical tool for expanding pediatric muscle physiology and clinical research. With a relatively small and variable dataset, our fully automatic segmentation technique produces accuracies that matched or exceeded the current state of the art. The two-stage segmentation avoids memory issues and excessive run times by using a first stage focused on cropping out unnecessary data. The excellent Dice similarity coefficients improve upon previous template-based automatic and semiautomatic methodologies targeting the leg musculature. More importantly, with a naturally variable dataset (size, shape, etc.), the proposed model demonstrates slightly improved accuracies, compared to previous neural networks methods.
Journal ArticleDOI
Statistical multi-level shape models for scalable modeling of multi-organ anatomies
Nawazish N. Khan,Andrew C. Peterson,B. Aubert,Alan Morris,Penny R. Atkins,Amy L. Lenz,Andrew E. Anderson,Shireen Y. Elhabian +7 more
TL;DR: In this paper , a new approach for correspondence point optimization of multiple organs is proposed, where the shape statistics consists of two mutually orthogonal subspaces: the within-organ subspace and the between-organ subspace.
References
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Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Posted Content
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Journal ArticleDOI
Active shape models—their training and application
TL;DR: This work describes a method for building models by learning patterns of variability from a training set of correctly annotated images that can be used for image search in an iterative refinement algorithm analogous to that employed by Active Contour Models (Snakes).
Journal ArticleDOI
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Hoo-Chang Shin,Holger R. Roth,Mingchen Gao,Le Lu,Ziyue Xu,Isabella Nogues,Jianhua Yao,Daniel J. Mollura,Ronald M. Summers +8 more
TL;DR: Two specific computer-aided detection problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification are studied, achieving the state-of-the-art performance on the mediastinal LN detection, and the first five-fold cross-validation classification results are reported.
Journal ArticleDOI
Statistical shape models for 3D medical image segmentation: a review.
TL;DR: Statistical shape models (SSMs) have by now been firmly established as a robust tool for segmentation of medical images as discussed by the authors, primarily made possible by breakthroughs in automatic detection of shape correspondences.