841 research outputs found
Segmentation Loss Odyssey
Loss functions are one of the crucial ingredients in deep learning-based
medical image segmentation methods. Many loss functions have been proposed in
existing literature, but are studied separately or only investigated with few
other losses. In this paper, we present a systematic taxonomy to sort existing
loss functions into four meaningful categories. This helps to reveal links and
fundamental similarities between them. Moreover, we explore the relationship
between the traditional region-based and the more recent boundary-based loss
functions. The PyTorch implementations of these loss functions are publicly
available at \url{https://github.com/JunMa11/SegLoss}.Comment: Educational Materials
(https://miccai-sb.github.io/materials/Ma2019.pdf
AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy
Methods: Our deep learning model, called AnatomyNet, segments OARs from head
and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT
images as input and generating masks of all OARs of interest in one shot.
AnatomyNet is built upon the popular 3D U-net architecture, but extends it in
three important ways: 1) a new encoding scheme to allow auto-segmentation on
whole-volume CT images instead of local patches or subsets of slices, 2)
incorporating 3D squeeze-and-excitation residual blocks in encoding layers for
better feature representation, and 3) a new loss function combining Dice scores
and focal loss to facilitate the training of the neural model. These features
are designed to address two main challenges in deep-learning-based HaN
segmentation: a) segmenting small anatomies (i.e., optic chiasm and optic
nerves) occupying only a few slices, and b) training with inconsistent data
annotations with missing ground truth for some anatomical structures.
Results: We collected 261 HaN CT images to train AnatomyNet, and used MICCAI
Head and Neck Auto Segmentation Challenge 2015 as a benchmark dataset to
evaluate the performance of AnatomyNet. The objective is to segment nine
anatomies: brain stem, chiasm, mandible, optic nerve left, optic nerve right,
parotid gland left, parotid gland right, submandibular gland left, and
submandibular gland right. Compared to previous state-of-the-art results from
the MICCAI 2015 competition, AnatomyNet increases Dice similarity coefficient
by 3.3% on average. AnatomyNet takes about 0.12 seconds to fully segment a head
and neck CT image of dimension 178 x 302 x 225, significantly faster than
previous methods. In addition, the model is able to process whole-volume CT
images and delineate all OARs in one pass, requiring little pre- or
post-processing.
https://github.com/wentaozhu/AnatomyNet-for-anatomical-segmentation.git.Comment: 6 figures, 4 videos in GitHub and YouTube. Accepted by Medical
Physics. Code and videos are available on GitHub. Video:
https://www.youtube.com/watch?v=_PpIUIm4XL
A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation
We propose a generalized focal loss function based on the Tversky index to
address the issue of data imbalance in medical image segmentation. Compared to
the commonly used Dice loss, our loss function achieves a better trade off
between precision and recall when training on small structures such as lesions.
To evaluate our loss function, we improve the attention U-Net model by
incorporating an image pyramid to preserve contextual features. We experiment
on the BUS 2017 dataset and ISIC 2018 dataset where lesions occupy 4.84% and
21.4% of the images area and improve segmentation accuracy when compared to the
standard U-Net by 25.7% and 3.6%, respectively.Comment: submitted to 2019 IEEE International Symposium on Biomedical Imaging
(ISBI
Multiview and Multiclass Image Segmentation using Deep Learning in Fetal Echocardiography
Congenital heart disease (CHD) is the most common congenital abnormality
associated with birth defects in the United States. Despite training efforts
and substantial advancement in ultrasound technology over the past years, CHD
remains an abnormality that is frequently missed during prenatal
ultrasonography. Therefore, computer-aided detection of CHD can play a critical
role in prenatal care by improving screening and diagnosis. Since many CHDs
involve structural abnormalities, automatic segmentation of anatomical
structures is an important step in the analysis of fetal echocardiograms. While
existing methods mainly focus on the four-chamber view with a small number of
structures, here we present a more comprehensive deep learning segmentation
framework covering 14 anatomical structures in both three-vessel trachea and
four-chamber views. Specifically, our framework enhances the V-Net with spatial
dropout, group normalization, and deep supervision to train a segmentation
model that can be applied on both views regardless of abnormalities. By
identifying the pitfall of using the Dice loss when some labels are unavailable
in some images, this framework integrates information from multiple views and
is robust to missing structures due to anatomical anomalies, achieving an
average Dice score of 79%.Comment: This paper was accepted by SPIE Medical Imaging 202
SemSegLoss: A python package of loss functions for semantic segmentation
Image Segmentation has been an active field of research as it has a wide
range of applications, ranging from automated disease detection to self-driving
cars. In recent years, various research papers proposed different loss
functions used in case of biased data, sparse segmentation, and unbalanced
dataset. In this paper, we introduce SemSegLoss, a python package consisting of
some of the well-known loss functions widely used for image segmentation. It is
developed with the intent to help researchers in the development of novel loss
functions and perform an extensive set of experiments on model architectures
for various applications. The ease-of-use and flexibility of the presented
package have allowed reducing the development time and increased evaluation
strategies of machine learning models for semantic segmentation. Furthermore,
different applications that use image segmentation can use SemSegLoss because
of the generality of its functions. This wide range of applications will lead
to the development and growth of AI across all industries.Comment: 8 pages, 2 table
Multi Scale Supervised 3D U-Net for Kidney and Tumor Segmentation
U-Net has achieved huge success in various medical image segmentation
challenges. Kinds of new architectures with bells and whistles might succeed in
certain dataset when employed with optimal hyper-parameter, but their
generalization always can't be guaranteed. Here, we focused on the basic U-Net
architecture and proposed a multi scale supervised 3D U-Net for the
segmentation task in KiTS19 challenge. To enhance the performance, our work can
be summarized as three folds: first, we used multi scale supervision in the
decoder pathway, which could encourage the network to predict right results
from the deep layers; second, with the aim to alleviate the bad effect from the
sample imbalance of kidney and tumor, we adopted exponential logarithmic loss;
third, a connected-component based post processing method was designed to
remove the obviously wrong voxels. In the published KiTS19 training dataset
(totally 210 patients), we divided 42 patients to be test dataset and finally
obtained DICE scores of 0.969 and 0.805 for the kidney and tumor respectively.
In the challenge, we finally achieved the 7th place among 106 teams with the
Composite Dice of 0.8961, namely 0.9741 for kidney and 0.8181 for tumor.Comment: 7 pages, 5 figure
Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentation
Target imbalance affects the performance of recent deep learning methods in
many medical image segmentation tasks. It is a twofold problem: class imbalance
- positive class (lesion) size compared to negative class (non-lesion) size;
lesion size imbalance - large lesions overshadows small ones (in the case of
multiple lesions per image). While the former was addressed in multiple works,
the latter lacks investigation. We propose a loss reweighting approach to
increase the ability of the network to detect small lesions. During the
learning process, we assign a weight to every image voxel. The assigned weights
are inversely proportional to the lesion volume, thus smaller lesions get
larger weights. We report the benefit from our method for well-known loss
functions, including Dice Loss, Focal Loss, and Asymmetric Similarity Loss.
Additionally, we compare our results with other reweighting techniques:
Weighted Cross-Entropy and Generalized Dice Loss. Our experiments show that
inverse weighting considerably increases the detection quality, while preserves
the delineation quality on a state-of-the-art level. We publish a complete
experimental pipeline for two publicly available datasets of CT images: LiTS
and LUNA16 (https://github.com/neuro-ml/inverse_weighting). We also show
results on a private database of MR images for the task of multiple brain
metastases delineation.Comment: Accepted to MICCAI 202
Segmentation overlapping wear particles with few labelled data and imbalance sample
Ferrograph image segmentation is of significance for obtaining features of
wear particles. However, wear particles are usually overlapped in the form of
debris chains, which makes challenges to segment wear debris. An overlapping
wear particle segmentation network (OWPSNet) is proposed in this study to
segment the overlapped debris chains. The proposed deep learning model includes
three parts: a region segmentation network, an edge detection network and a
feature refine module. The region segmentation network is an improved U shape
network, and it is applied to separate the wear debris form background of
ferrograph image. The edge detection network is used to detect the edges of
wear particles. Then, the feature refine module combines low-level features and
high-level semantic features to obtain the final results. In order to solve the
problem of sample imbalance, we proposed a square dice loss function to
optimize the model. Finally, extensive experiments have been carried out on a
ferrograph image dataset. Results show that the proposed model is capable of
separating overlapping wear particles. Moreover, the proposed square dice loss
function can improve the segmentation results, especially for the segmentation
results of wear particle edge
Boundary loss for highly unbalanced segmentation
Widely used loss functions for convolutional neural network (CNN)
segmentation, e.g., Dice or cross-entropy, are based on integrals (summations)
over the segmentation regions. Unfortunately, it is quite common in medical
image analysis to have highly unbalanced segmentations, where standard losses
contain regional terms with values that differ considerably --typically of
several orders of magnitude-- across segmentation classes, which may affect
training performance and stability. The purpose of this study is to build a
boundary loss, which takes the form of a distance metric on the space of
contours, not regions. We argue that a boundary loss can mitigate the
difficulties of regional losses in the context of highly unbalanced
segmentation problems because it uses integrals over the boundary between
regions instead of unbalanced integrals over regions. Furthermore, a boundary
loss provides information that is complementary to regional losses.
Unfortunately, it is not straightforward to represent the boundary points
corresponding to the regional softmax outputs of a CNN. Our boundary loss is
inspired by discrete (graph-based) optimization techniques for computing
gradient flows of curve evolution. Following an integral approach for computing
boundary variations, we express a non-symmetric L2 distance on the space of
shapes as a regional integral, which avoids completely local differential
computations involving contour points. Our boundary loss is the sum of linear
functions of the regional softmax probability outputs of the network.
Therefore, it can easily be combined with standard regional losses and
implemented with any existing deep network architecture for N-D segmentation.
Our boundary loss has been validated on two benchmark datasets corresponding to
difficult, highly unbalanced segmentation problems: the ischemic stroke lesion
(ISLES) and white matter hyperintensities (WMH).Comment: Talk at MIDL 2019 [arXiv:1907.08612
Improved Inference via Deep Input Transfer
Although numerous improvements have been made in the field of image
segmentation using convolutional neural networks, the majority of these
improvements rely on training with larger datasets, model architecture
modifications, novel loss functions, and better optimizers. In this paper, we
propose a new segmentation performance boosting paradigm that relies on
optimally modifying the network's input instead of the network itself. In
particular, we leverage the gradients of a trained segmentation network with
respect to the input to transfer it to a space where the segmentation accuracy
improves. We test the proposed method on three publicly available medical image
segmentation datasets: the ISIC 2017 Skin Lesion Segmentation dataset, the
Shenzhen Chest X-Ray dataset, and the CVC-ColonDB dataset, for which our method
achieves improvements of 5.8%, 0.5%, and 4.8% in the average Dice scores,
respectively.Comment: Accepted to MICCAI 201
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