ConUNETR: A Conditional Transformer Network for 3D Micro-CT Embryonic Cartilage Segmentation

• URL: http://arxiv.org/abs/2402.03695v1
• Date: Tue, 6 Feb 2024 04:30:49 GMT
• Title: ConUNETR: A Conditional Transformer Network for 3D Micro-CT Embryonic Cartilage Segmentation
• Authors: Nishchal Sapkota, Yejia Zhang, Susan M. Motch Perrine, Yuhan Hsi, Sirui Li, Meng Wu, Greg Holmes, Abdul R. Abdulai, Ethylin W. Jabs, Joan T. Richtsmeier, Danny Z Chen
• Abstract summary: We propose a novel Transformer-based segmentation model with improved biological priors that better distills morphologically diverse information through conditional mechanisms. Experiments on the mice cartilage dataset show the superiority of our new model compared to other competitive segmentation models.
• Score: 13.497950682194704
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Studying the morphological development of cartilaginous and osseous structures is critical to the early detection of life-threatening skeletal dysmorphology. Embryonic cartilage undergoes rapid structural changes within hours, introducing biological variations and morphological shifts that limit the generalization of deep learning-based segmentation models that infer across multiple embryonic age groups. Obtaining individual models for each age group is expensive and less effective, while direct transfer (predicting an age unseen during training) suffers a potential performance drop due to morphological shifts. We propose a novel Transformer-based segmentation model with improved biological priors that better distills morphologically diverse information through conditional mechanisms. This enables a single model to accurately predict cartilage across multiple age groups. Experiments on the mice cartilage dataset show the superiority of our new model compared to other competitive segmentation models. Additional studies on a separate mice cartilage dataset with a distinct mutation show that our model generalizes well and effectively captures age-based cartilage morphology patterns.

Related papers

• UniCoN: Universal Conditional Networks for Multi-Age Embryonic Cartilage Segmentation with Sparsely Annotated Data [13.379161180001303]
  Osteochondrodysplasia, affecting 2-3% of newborns globally, is a group of bone and cartilage disorders. Current research on this disease involves accurately segmenting the developing cartilage in 3D micro-CT images of embryonic mice. We propose two new mechanisms, one conditioned on discrete age categories and the other on continuous image crop locations, to enable an accurate representation of cartilage shape changes.
  arXiv  Detail & Related papers  (2024-10-16T21:06:55Z)
• Adapting Mouse Pathological Model to Human Glomerular Lesion Segmentation [13.209051765045512]
  We introduce GLAM, a deep learning study for fine-grained segmentation of human kidney lesions using a mouse model. We evaluate different learning strategies for segmenting human pathological lesions using zero-shot transfer learning and hybrid learning.
  arXiv  Detail & Related papers  (2024-07-25T20:47:43Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Brain Tissue Segmentation Across the Human Lifespan via Supervised Contrastive Learning [34.82366750668948]
  We make the first attempt to segment brain tissues across the entire human lifespan (0-100 years of age) using a unified deep learning model. Our model accurately segments brain tissues across the lifespan and outperforms existing methods.
  arXiv  Detail & Related papers  (2023-01-03T21:54:17Z)
• Using machine learning on new feature sets extracted from 3D models of broken animal bones to classify fragments according to break agent [53.796331564067835]
  We present a new approach to fracture pattern analysis aimed at distinguishing bone fragments resulting from hominin bone breakage and those produced by carnivores. This new method uses 3D models of fragmentary bone to extract a much richer dataset that is more transparent and replicable than feature sets previously used in fracture pattern analysis. Supervised machine learning algorithms are properly used to classify bone fragments according to agent of breakage with average mean accuracy of 77% across tests.
  arXiv  Detail & Related papers  (2022-05-20T20:16:21Z)
• Distinguishing Healthy Ageing from Dementia: a Biomechanical Simulation of Brain Atrophy using Deep Networks [5.411313268782566]
  We present a deep learning framework for hyper-elastic strain modelling of brain atrophy, during healthy ageing and in Alzheimer's Disease. The framework directly models the effects of age, disease status, and scan interval to regress regional patterns of atrophy, from which a strain-based model estimates deformations. Results show that the framework can estimate realistic deformations, following the known course of Alzheimer's disease, that clearly differentiate between healthy and demented patterns of ageing.
  arXiv  Detail & Related papers  (2021-08-18T15:58:53Z)
• A Diffeomorphic Aging Model for Adult Human Brain from Cross-Sectional Data [3.2188961353850187]
  We propose a method to develop an aging model for a given population by using images from different subjects at different time points. The proposed model is successfully validated on two public cross-sectional datasets.
  arXiv  Detail & Related papers  (2021-06-28T10:04:05Z)
• Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition [65.77962788209103]
  We propose class subset learning by dividing the long-tailed data into multiple class subsets according to prior knowledge. It enforces the model to focus on learning the subset-specific knowledge. The proposed framework proved to be effective for the long-tailed retinal diseases recognition task.
  arXiv  Detail & Related papers  (2021-04-22T13:39:33Z)
• Epigenetic evolution of deep convolutional models [81.21462458089142]
  We build upon a previously proposed neuroevolution framework to evolve deep convolutional models. We propose a convolutional layer layout which allows kernels of different shapes and sizes to coexist within the same layer. The proposed layout enables the size and shape of individual kernels within a convolutional layer to be evolved with a corresponding new mutation operator.
  arXiv  Detail & Related papers  (2021-04-12T12:45:16Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• Constrained Multi-shape Evolution for Overlapping Cytoplasm Segmentation [49.992392231966015]
  We present a novel and effective shape prior-based approach, called constrained multi-shape evolution. It segments all overlapping cytoplasms in the clump simultaneously by jointly evolving each cytoplasm's shape guided by the modeled shape priors. In the shape evolution, we compensate intensity deficiency for the segmentation by introducing not only the modeled local shape priors but also global shape priors.
  arXiv  Detail & Related papers  (2020-04-08T09:08:07Z)

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.