Abstract：
:We present a novel method to segment instances of glandular structures from colon histopathology images. We use a structure learning approach which represents local spatial configurations of class labels, capturing structural information normally ignored by sliding-window methods. This allows us to reveal different spatial structures of pixel labels (e.g., locations between adjacent glands, or far from glands), and to identify correctly neighboring glandular structures as separate instances. Exemplars of label structures are obtained via clustering and used to train support vector machine classifiers. The label structures predicted are then combined and post-processed to obtain segmentation maps. We combine hand-crafted, multi-scale image features with features computed by a deep convolutional network trained to map images to segmentation maps. We evaluate the proposed method on the public domain GlaS data set, which allows extensive comparisons with recent, alternative methods. Using the GlaS contest protocol, our method achieves the overall best performance.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Manivannan S,Li W,Zhang J,Trucco E,McKenna SJdoi
10.1109/TMI.2017.2750210subject
Has Abstractpub_date
2018-01-01 00:00:00pages
210-221issue
1eissn
0278-0062issn
1558-254Xjournal_volume
37pub_type
杂志文章abstract：:This paper presents a fully automated algorithm for segmentation of multiple sclerosis (MS) lesions from multispectral magnetic resonance (MR) images. The method performs intensity-based tissue classification using a stochastic model for normal brain images and simultaneously detects MS lesions as outliers that are no...
journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/42.938237
更新日期：2001-08-01 00:00:00
abstract：:A method is introduced to compensate for missing projection data that can result from gas between detectors or from malfunctioning detectors. This method uses constraints in the Fourier domain to estimate the missing data, thus completing the data set so that the filtered backprojection algorithm can be used to recons...
journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/42.3925
更新日期：1988-01-01 00:00:00
abstract：:The authors describe a technique to reduce truncation artifacts in chemical shift images taken for in vivo magnetic resonance spectroscopy by using boundary information available from anatomic images. In this approach, high spatial frequency components are extrapolated from the observed data by making use of the bound...
journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/42.97578
更新日期：1991-01-01 00:00:00
abstract：:Image reconstruction in low-count PET is particularly challenging because gammas from natural radioactivity in Lu-based crystals cause high random fractions that lower the measurement signal-to-noise-ratio (SNR). In model-based image reconstruction (MBIR), using more iterations of an unregularized method may increase ...
journal_title：IEEE transactions on medical imaging
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doi：10.1109/TMI.2020.2998480
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abstract：:Ultrafast ultrasound imaging using plane or diverging waves, instead of focused beams, has advanced greatly the development of novel ultrasound imaging methods for evaluating tissue functions beyond anatomical information. However, the sonographic signal-to-noise ratio (SNR) of ultrafast imaging remains limited due to...
journal_title：IEEE transactions on medical imaging
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doi：10.1109/TMI.2017.2781261
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abstract：:Spectral computed tomography (CT) provides information on material characterization and quantification because of its ability to separate different basis materials. Dual-energy (DE) CT provides two sets of measurements at two different source energies. In principle, two materials can be accurately decomposed from DECT...
journal_title：IEEE transactions on medical imaging
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doi：10.1109/TMI.2014.2320284
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abstract：:Conventional Doppler ultrasound is useful for visualizing fast blood flow in large resolvable vessels. However, frame rate and tissue clutter caused by movement of the patient or sonographer make visualizing slow flow with ultrasound difficult. Patient and sonographer motion causes spectral broadening of the clutter s...
journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/TMI.2017.2714901
更新日期：2017-09-01 00:00:00
abstract：:The identification of bronchi on Computed Tomography (CT) images of the lungs provides valuable clinical information in patients with suspected airways diseases including bronchiectasis, emphysema, or constrictive obliterative bronchiolitis. The automated recognition of the airways is, therefore, an important part of ...
journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/42.952731
更新日期：2001-09-01 00:00:00
abstract：:An algorithm is presented for the analysis and quantification of the vascular structures of the human retina. Information about retinal blood vessel morphology is used in grading the severity and progression of a number of diseases. These disease processes are typically followed over relatively long time courses, and ...
journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/42.363106
更新日期：1994-01-01 00:00:00
abstract：:Magnetic particle imaging (MPI) is able to provide high temporal and good spatial resolution, high signal to noise ratio and sensitivity. Furthermore, it is a truly quantitative method as its signal strength is proportional to the concentration of its tracer, superparamagnetic iron oxide nanoparticles (SPIOs), over a ...
journal_title：IEEE transactions on medical imaging
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doi：10.1109/TMI.2016.2559538
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journal_title：IEEE transactions on medical imaging
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doi：10.1109/TMI.2017.2685522
更新日期：2017-06-01 00:00:00
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journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/42.75611
更新日期：1991-01-01 00:00:00
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journal_title：IEEE transactions on medical imaging
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doi：10.1109/TMI.2010.2042064
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journal_title：IEEE transactions on medical imaging
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doi：10.1109/TMI.2020.3006445
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journal_title：IEEE transactions on medical imaging
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doi：10.1109/TMI.2019.2898414
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journal_title：IEEE transactions on medical imaging
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doi：
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journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/TMI.2011.2125980
更新日期：2011-08-01 00:00:00
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journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/42.668698
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journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/TMI.1987.4307791
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journal_title：IEEE transactions on medical imaging
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doi：10.1109/42.836368
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journal_title：IEEE transactions on medical imaging
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doi：10.1109/TMI.2012.2236349
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doi：10.1109/TMI.2003.815901
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journal_title：IEEE transactions on medical imaging
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doi：10.1109/TMI.2011.2171497
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journal_title：IEEE transactions on medical imaging
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journal_title：IEEE transactions on medical imaging
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doi：10.1109/TMI.2020.3017815
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journal_title：IEEE transactions on medical imaging
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journal_title：IEEE transactions on medical imaging
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journal_title：IEEE transactions on medical imaging
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journal_title：IEEE transactions on medical imaging
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doi：10.1109/TMI.2007.913248
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