11 research outputs found
Transition region based approach for skin lesion segmentation
Skin melanoma is a skin disease that affects nearly 40% of people globally. Manual detection of the area is a time-consuming process and requires expert knowledge. The application of computer vision techniques can simplify this. In this article, a novel unsupervised transition region based approach for skin lesion segmentation for melanoma detection is proposed. The method starts with Gaussian blurring of the green channel dermoscopic image. Further, the transition region is extracted using local variance features and a global thresholding operation. It achieves the region of interest (binary mask) using various morphological operations. Finally, the melanoma regions are segregated from normal skin regions using the binary mask. The proposed method is tested using DermQuest dataset along with ISIC 2017 dataset and it achieves better results as compared to other state of art methods in effectively segmenting the melanoma regions from the normal skin regions
Weakly Supervised Volumetric Image Segmentation with Deformed Templates
There are many approaches that use weak-supervision to train networks to
segment 2D images. By contrast, existing 3D approaches rely on full-supervision
of a subset of 2D slices of the 3D image volume. In this paper, we propose an
approach that is truly weakly-supervised in the sense that we only need to
provide a sparse set of 3D point on the surface of target objects, an easy task
that can be quickly done. We use the 3D points to deform a 3D template so that
it roughly matches the target object outlines and we introduce an architecture
that exploits the supervision provided by coarse template to train a network to
find accurate boundaries.
We evaluate the performance of our approach on Computed Tomography (CT),
Magnetic Resonance Imagery (MRI) and Electron Microscopy (EM) image datasets.
We will show that it outperforms a more traditional approach to
weak-supervision in 3D at a reduced supervision cost.Comment: 13 Page
I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation
Adversarial training has been recently employed for realizing structured
semantic segmentation, in which the aim is to preserve higher-level scene
structural consistencies in dense predictions. However, as we show, value-based
discrimination between the predictions from the segmentation network and
ground-truth annotations can hinder the training process from learning to
improve structural qualities as well as disabling the network from properly
expressing uncertainties. In this paper, we rethink adversarial training for
semantic segmentation and propose to formulate the fake/real discrimination
framework with a correct/incorrect training objective. More specifically, we
replace the discriminator with a "gambler" network that learns to spot and
distribute its budget in areas where the predictions are clearly wrong, while
the segmenter network tries to leave no clear clues for the gambler where to
bet. Empirical evaluation on two road-scene semantic segmentation tasks shows
that not only does the proposed method re-enable expressing uncertainties, it
also improves pixel-wise and structure-based metrics.Comment: 13 pages, 8 figure
Deep Semantic Segmentation of Natural and Medical Images: A Review
The semantic image segmentation task consists of classifying each pixel of an
image into an instance, where each instance corresponds to a class. This task
is a part of the concept of scene understanding or better explaining the global
context of an image. In the medical image analysis domain, image segmentation
can be used for image-guided interventions, radiotherapy, or improved
radiological diagnostics. In this review, we categorize the leading deep
learning-based medical and non-medical image segmentation solutions into six
main groups of deep architectural, data synthesis-based, loss function-based,
sequenced models, weakly supervised, and multi-task methods and provide a
comprehensive review of the contributions in each of these groups. Further, for
each group, we analyze each variant of these groups and discuss the limitations
of the current approaches and present potential future research directions for
semantic image segmentation.Comment: 45 pages, 16 figures. Accepted for publication in Springer Artificial
Intelligence Revie
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions
Medical Image Analysis is currently experiencing a paradigm shift due to Deep
Learning. This technology has recently attracted so much interest of the
Medical Imaging community that it led to a specialized conference in `Medical
Imaging with Deep Learning' in the year 2018. This article surveys the recent
developments in this direction, and provides a critical review of the related
major aspects. We organize the reviewed literature according to the underlying
Pattern Recognition tasks, and further sub-categorize it following a taxonomy
based on human anatomy. This article does not assume prior knowledge of Deep
Learning and makes a significant contribution in explaining the core Deep
Learning concepts to the non-experts in the Medical community. Unique to this
study is the Computer Vision/Machine Learning perspective taken on the advances
of Deep Learning in Medical Imaging. This enables us to single out `lack of
appropriately annotated large-scale datasets' as the core challenge (among
other challenges) in this research direction. We draw on the insights from the
sister research fields of Computer Vision, Pattern Recognition and Machine
Learning etc.; where the techniques of dealing with such challenges have
already matured, to provide promising directions for the Medical Imaging
community to fully harness Deep Learning in the future