2,980 research outputs found
AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation
Despite the considerable progress in automatic abdominal multi-organ
segmentation from CT/MRI scans in recent years, a comprehensive evaluation of
the models' capabilities is hampered by the lack of a large-scale benchmark
from diverse clinical scenarios. Constraint by the high cost of collecting and
labeling 3D medical data, most of the deep learning models to date are driven
by datasets with a limited number of organs of interest or samples, which still
limits the power of modern deep models and makes it difficult to provide a
fully comprehensive and fair estimate of various methods. To mitigate the
limitations, we present AMOS, a large-scale, diverse, clinical dataset for
abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected
from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease
patients, each with voxel-level annotations of 15 abdominal organs, providing
challenging examples and test-bed for studying robust segmentation algorithms
under diverse targets and scenarios. We further benchmark several
state-of-the-art medical segmentation models to evaluate the status of the
existing methods on this new challenging dataset. We have made our datasets,
benchmark servers, and baselines publicly available, and hope to inspire future
research. Information can be found at https://amos22.grand-challenge.org
Med-Query: Steerable Parsing of 9-DoF Medical Anatomies with Query Embedding
Automatic parsing of human anatomies at instance-level from 3D computed
tomography (CT) scans is a prerequisite step for many clinical applications.
The presence of pathologies, broken structures or limited field-of-view (FOV)
all can make anatomy parsing algorithms vulnerable. In this work, we explore
how to exploit and conduct the prosperous detection-then-segmentation paradigm
in 3D medical data, and propose a steerable, robust, and efficient computing
framework for detection, identification, and segmentation of anatomies in CT
scans. Considering complicated shapes, sizes and orientations of anatomies,
without lose of generality, we present the nine degrees-of-freedom (9-DoF) pose
estimation solution in full 3D space using a novel single-stage,
non-hierarchical forward representation. Our whole framework is executed in a
steerable manner where any anatomy of interest can be directly retrieved to
further boost the inference efficiency. We have validated the proposed method
on three medical imaging parsing tasks of ribs, spine, and abdominal organs.
For rib parsing, CT scans have been annotated at the rib instance-level for
quantitative evaluation, similarly for spine vertebrae and abdominal organs.
Extensive experiments on 9-DoF box detection and rib instance segmentation
demonstrate the effectiveness of our framework (with the identification rate of
97.0% and the segmentation Dice score of 90.9%) in high efficiency, compared
favorably against several strong baselines (e.g., CenterNet, FCOS, and
nnU-Net). For spine identification and segmentation, our method achieves a new
state-of-the-art result on the public CTSpine1K dataset. Last, we report highly
competitive results in multi-organ segmentation at FLARE22 competition. Our
annotations, code and models will be made publicly available at:
https://github.com/alibaba-damo-academy/Med_Query.Comment: updated versio
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
Automatic organ segmentation is an important yet challenging problem for
medical image analysis. The pancreas is an abdominal organ with very high
anatomical variability. This inhibits previous segmentation methods from
achieving high accuracies, especially compared to other organs such as the
liver, heart or kidneys. In this paper, we present a probabilistic bottom-up
approach for pancreas segmentation in abdominal computed tomography (CT) scans,
using multi-level deep convolutional networks (ConvNets). We propose and
evaluate several variations of deep ConvNets in the context of hierarchical,
coarse-to-fine classification on image patches and regions, i.e. superpixels.
We first present a dense labeling of local image patches via
and nearest neighbor fusion. Then we describe a regional
ConvNet () that samples a set of bounding boxes around
each image superpixel at different scales of contexts in a "zoom-out" fashion.
Our ConvNets learn to assign class probabilities for each superpixel region of
being pancreas. Last, we study a stacked leveraging
the joint space of CT intensities and the dense
probability maps. Both 3D Gaussian smoothing and 2D conditional random fields
are exploited as structured predictions for post-processing. We evaluate on CT
images of 82 patients in 4-fold cross-validation. We achieve a Dice Similarity
Coefficient of 83.66.3% in training and 71.810.7% in testing.Comment: To be presented at MICCAI 2015 - 18th International Conference on
Medical Computing and Computer Assisted Interventions, Munich, German
Morphological and multi-level geometrical descriptor analysis in CT and MRI volumes for automatic pancreas segmentation
Automatic pancreas segmentation in 3D radiological scans is a critical, yet challenging task. As a prerequisite for computer-aided diagnosis (CADx) systems, accurate pancreas segmentation could generate both quantitative and qualitative information towards establishing the severity of a condition, and thus provide additional guidance for therapy planning. Since the pancreas is an organ of high inter-patient anatomical variability, previous segmentation approaches report lower quantitative accuracy scores in comparison to abdominal organs such as the liver or kidneys. This paper presents a novel approach for automatic pancreas segmentation in magnetic resonance imaging (MRI) and computer tomography (CT) scans. This method exploits 3D segmentation that, when coupled with geometrical and morphological characteristics of abdominal tissue, classifies distinct contours in tight pixel-range proximity as “pancreas” or “non-pancreas”. There are three main stages to this approach: (1) identify a major pancreas region and apply contrast enhancement to differentiate between pancreatic and surrounding tissue; (2) perform 3D segmentation via continuous max-flow and min-cuts approach, structured forest edge detection, and a training dataset of annotated pancreata; (3) eliminate non-pancreatic contours from resultant segmentation via morphological operations on area, structure and connectivity between distinct contours. The proposed method is evaluated on a dataset containing 82 CT image volumes, achieving mean Dice Similarity coefficient (DSC) of 79.3 ± 4.4%. Two MRI datasets containing 216 and 132 image volumes are evaluated, achieving mean DSC 79.6 ± 5.7% and 81.6 ± 5.1% respectively. This approach is statistically stable, reflected by lower metrics in standard deviation in comparison to state-of-the-art approaches
A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans
Deep neural networks have been widely adopted for automatic organ
segmentation from abdominal CT scans. However, the segmentation accuracy of
some small organs (e.g., the pancreas) is sometimes below satisfaction,
arguably because deep networks are easily disrupted by the complex and variable
background regions which occupies a large fraction of the input volume. In this
paper, we formulate this problem into a fixed-point model which uses a
predicted segmentation mask to shrink the input region. This is motivated by
the fact that a smaller input region often leads to more accurate segmentation.
In the training process, we use the ground-truth annotation to generate
accurate input regions and optimize network weights. On the testing stage, we
fix the network parameters and update the segmentation results in an iterative
manner. We evaluate our approach on the NIH pancreas segmentation dataset, and
outperform the state-of-the-art by more than 4%, measured by the average
Dice-S{\o}rensen Coefficient (DSC). In addition, we report 62.43% DSC in the
worst case, which guarantees the reliability of our approach in clinical
applications.Comment: Accepted to MICCAI 2017 (8 pages, 3 figures
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