753 research outputs found
Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning
Convolutional neural networks (CNNs) have achieved state-of-the-art
performance for automatic medical image segmentation. However, they have not
demonstrated sufficiently accurate and robust results for clinical use. In
addition, they are limited by the lack of image-specific adaptation and the
lack of generalizability to previously unseen object classes. To address these
problems, we propose a novel deep learning-based framework for interactive
segmentation by incorporating CNNs into a bounding box and scribble-based
segmentation pipeline. We propose image-specific fine-tuning to make a CNN
model adaptive to a specific test image, which can be either unsupervised
(without additional user interactions) or supervised (with additional
scribbles). We also propose a weighted loss function considering network and
interaction-based uncertainty for the fine-tuning. We applied this framework to
two applications: 2D segmentation of multiple organs from fetal MR slices,
where only two types of these organs were annotated for training; and 3D
segmentation of brain tumor core (excluding edema) and whole brain tumor
(including edema) from different MR sequences, where only tumor cores in one MR
sequence were annotated for training. Experimental results show that 1) our
model is more robust to segment previously unseen objects than state-of-the-art
CNNs; 2) image-specific fine-tuning with the proposed weighted loss function
significantly improves segmentation accuracy; and 3) our method leads to
accurate results with fewer user interactions and less user time than
traditional interactive segmentation methods.Comment: 11 pages, 11 figure
Rich probabilistic models for semantic labeling
Das Ziel dieser Monographie ist es die Methoden und Anwendungen des semantischen Labelings zu erforschen. Unsere Beiträge zu diesem sich rasch entwickelten Thema sind bestimmte Aspekte der Modellierung und der Inferenz in probabilistischen Modellen und ihre Anwendungen in den interdisziplinären Bereichen der Computer Vision sowie medizinischer Bildverarbeitung und Fernerkundung
Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models
Segmentation is a fundamental task for extracting semantically meaningful
regions from an image. The goal of segmentation algorithms is to accurately
assign object labels to each image location. However, image-noise, shortcomings
of algorithms, and image ambiguities cause uncertainty in label assignment.
Estimating the uncertainty in label assignment is important in multiple
application domains, such as segmenting tumors from medical images for
radiation treatment planning. One way to estimate these uncertainties is
through the computation of posteriors of Bayesian models, which is
computationally prohibitive for many practical applications. On the other hand,
most computationally efficient methods fail to estimate label uncertainty. We
therefore propose in this paper the Active Mean Fields (AMF) approach, a
technique based on Bayesian modeling that uses a mean-field approximation to
efficiently compute a segmentation and its corresponding uncertainty. Based on
a variational formulation, the resulting convex model combines any
label-likelihood measure with a prior on the length of the segmentation
boundary. A specific implementation of that model is the Chan-Vese segmentation
model (CV), in which the binary segmentation task is defined by a Gaussian
likelihood and a prior regularizing the length of the segmentation boundary.
Furthermore, the Euler-Lagrange equations derived from the AMF model are
equivalent to those of the popular Rudin-Osher-Fatemi (ROF) model for image
denoising. Solutions to the AMF model can thus be implemented by directly
utilizing highly-efficient ROF solvers on log-likelihood ratio fields. We
qualitatively assess the approach on synthetic data as well as on real natural
and medical images. For a quantitative evaluation, we apply our approach to the
icgbench dataset
Segmentation of Brain Magnetic Resonance Images (MRIs): A Review
Abstract MR imaging modality has assumed an important position in studying the characteristics of soft tissues. Generally, images acquired by using this modality are found to be affected by noise, partial volume effect (PVE) and intensity nonuniformity (INU). The presence of these factors degrades the quality of the image. As a result of which, it becomes hard to precisely distinguish between different neighboring regions constituting an image. To address this problem, various methods have been proposed. To study the nature of various proposed state-of-the-art medical image segmentation methods, a review was carried out. This paper presents a brief summary of this review and attempts to analyze the strength and weaknesses of the proposed methods. The review concludes that unfortunately, none of the proposed methods has been able to independently address the problem of precise segmentation in its entirety. The paper strongly favors the use of some module for restoring pixel intensity value along with a segmentation method to produce efficient results
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