57 research outputs found
[Work in progress] Scalable, out-of-the box segmentation of individual particles from mineral samples acquired with micro CT
Minerals are indispensable for a functioning modern society. Yet, their
supply is limited causing a need for optimizing their exploration and
extraction both from ores and recyclable materials. Typically, these processes
must be meticulously adapted to the precise properties of the processed
particles, an extensive characterization of their shapes, appearances as well
as the overall material composition. Current approaches perform this analysis
based on bulk segmentation and characterization of particles imaged with a
micro CT, and rely on rudimentary postprocessing techniques to separate
touching particles. However, due to their inability to reliably perform this
separation as well as the need to retrain or reconfigure methods for each new
image, these approaches leave untapped potential to be leveraged. Here, we
propose ParticleSeg3D, an instance segmentation method that is able to extract
individual particles from large micro CT images taken from mineral samples
embedded in an epoxy matrix. Our approach is based on the powerful nnU-Net
framework, introduces a particle size normalization, makes use of a border-core
representation to enable instance segmentation and is trained with a large
dataset containing particles of numerous different materials and minerals. We
demonstrate that ParticleSeg3D can be applied out-of-the box to a large variety
of particle types, including materials and appearances that have not been part
of the training set. Thus, no further manual annotations and retraining are
required when applying the method to new mineral samples, enabling
substantially higher scalability of experiments than existing methods. Our code
and dataset are made publicly available
Deep Active Learning for Computer Vision: Past and Future
As an important data selection schema, active learning emerges as the
essential component when iterating an Artificial Intelligence (AI) model. It
becomes even more critical given the dominance of deep neural network based
models, which are composed of a large number of parameters and data hungry, in
application. Despite its indispensable role for developing AI models, research
on active learning is not as intensive as other research directions. In this
paper, we present a review of active learning through deep active learning
approaches from the following perspectives: 1) technical advancements in active
learning, 2) applications of active learning in computer vision, 3) industrial
systems leveraging or with potential to leverage active learning for data
iteration, 4) current limitations and future research directions. We expect
this paper to clarify the significance of active learning in a modern AI model
manufacturing process and to bring additional research attention to active
learning. By addressing data automation challenges and coping with automated
machine learning systems, active learning will facilitate democratization of AI
technologies by boosting model production at scale.Comment: Accepted by APSIPA Transactions on Signal and Information Processin
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation
Medical image segmentation is a fundamental and critical step in many
image-guided clinical approaches. Recent success of deep learning-based
segmentation methods usually relies on a large amount of labeled data, which is
particularly difficult and costly to obtain especially in the medical imaging
domain where only experts can provide reliable and accurate annotations.
Semi-supervised learning has emerged as an appealing strategy and been widely
applied to medical image segmentation tasks to train deep models with limited
annotations. In this paper, we present a comprehensive review of recently
proposed semi-supervised learning methods for medical image segmentation and
summarized both the technical novelties and empirical results. Furthermore, we
analyze and discuss the limitations and several unsolved problems of existing
approaches. We hope this review could inspire the research community to explore
solutions for this challenge and further promote the developments in medical
image segmentation field
Medical Image Segmentation by Deep Convolutional Neural Networks
Medical image segmentation is a fundamental and critical step for medical image analysis. Due to the complexity and diversity of medical images, the segmentation of medical images continues to be a challenging problem. Recently, deep learning techniques, especially Convolution Neural Networks (CNNs) have received extensive research and achieve great success in many vision tasks. Specifically, with the advent of Fully Convolutional Networks (FCNs), automatic medical image segmentation based on FCNs is a promising research field. This thesis focuses on two medical image segmentation tasks: lung segmentation in chest X-ray images and nuclei segmentation in histopathological images.
For the lung segmentation task, we investigate several FCNs that have been successful in semantic and medical image segmentation. We evaluate the performance of these different FCNs on three publicly available chest X-ray image datasets.
For the nuclei segmentation task, since the challenges of this task are difficulty in segmenting the small, overlapping and touching nuclei, and limited ability of generalization to nuclei in different organs and tissue types, we propose a novel nuclei segmentation approach based on a two-stage learning framework and Deep Layer Aggregation (DLA). We convert the original binary segmentation task into a two-step task by adding nuclei-boundary prediction (3-classes) as an intermediate step. To solve our two-step task, we design a two-stage learning framework by stacking two U-Nets. The first stage estimates nuclei and their coarse boundaries while the second stage outputs the final fine-grained segmentation map. Furthermore, we also extend the U-Nets with DLA by iteratively merging features across different levels. We evaluate our proposed method on two public diverse nuclei datasets. The experimental results show that our proposed approach outperforms many standard segmentation architectures and recently proposed nuclei segmentation methods, and can be easily generalized across different cell types in various organs
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