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. 2017 Oct;4(4):041311.
doi: 10.1117/1.JMI.4.4.041311. Epub 2017 Dec 14.

Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation

Varghese Alex  1 , Kiran Vaidhya  1 , Subramaniam Thirunavukkarasu  1 , Chandrasekharan Kesavadas  2 , Ganapathy Krishnamurthi  1
Affiliations

Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation

Varghese Alex et al. J Med Imaging (Bellingham). 2017 Oct.

Abstract

The work explores the use of denoising autoencoders (DAEs) for brain lesion detection, segmentation, and false-positive reduction. Stacked denoising autoencoders (SDAEs) were pretrained using a large number of unlabeled patient volumes and fine-tuned with patches drawn from a limited number of patients ([Formula: see text], 40, 65). The results show negligible loss in performance even when SDAE was fine-tuned using 20 labeled patients. Low grade glioma (LGG) segmentation was achieved using a transfer learning approach in which a network pretrained with high grade glioma data was fine-tuned using LGG image patches. The networks were also shown to generalize well and provide good segmentation on unseen BraTS 2013 and BraTS 2015 test data. The manuscript also includes the use of a single layer DAE, referred to as novelty detector (ND). ND was trained to accurately reconstruct nonlesion patches. The reconstruction error maps of test data were used to localize lesions. The error maps were shown to assign unique error distributions to various constituents of the glioma, enabling localization. The ND learns the nonlesion brain accurately as it was also shown to provide good segmentation performance on ischemic brain lesions in images from a different database.

Keywords: brain lesion; deep learning; denoising autoencoder; gliomas; magnetic resonance imaging; stacked denoising autoencoder.

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Figures

Fig. 1
Fig. 1
Flowchart of the pipeline used for segmentation of gliomas.
Fig. 2
Fig. 2
Histogram matching: (a) histogram of reference FLAIR sequence, (b) histogram of test FLAIR sequence, and (c) histogram of test data posthistogram matching.
Fig. 3
Fig. 3
Performance of proposed networks: (a) ground truth, (b) prediction, (c) ground truth, (d) prediction, (e) ground truth, (f) prediction, (g) ground truth, (h) prediction, (i) raw prediction, (j) Otsu’s mask, (k) prediction after postprocessing, (l) ground truth, (m) FLAIR, (n) ND error map, (o) binarized error map, and (p) ground truth. In all images, orange indicates edema; yellow indicates nonenhancing tumor; red indicates necrotic region; white indicates enhancing tumor; and green indicates Otsu’s mask. In image (o), white indicates binarized error map.
Fig. 4
Fig. 4
Performance of ND and CND on BraTS dataset. (a) ND error map, (b) CND error map, (c) ground truth, (d) CND error map, (e) ground truth, (f) FLAIR, (g) T2, (h) CND error map, (i) ground truth, (j) binarized CND error map, (k) FLAIR, (l) T2, (m) binarized ND error map, (n). binarized CND error map, (o) ground truth in images (c), (e), (i) and (o), orange indicates edema; yellow indicates nonenhancing tumor; red indicates necrotic region; and white indicates enhancing tumor. In images (m) and (n), green indicates binarized error map.
Fig. 5
Fig. 5
Performance of various FP reduction technique. (a) FLAIR, (b) raw prediction, (c) binarized FLAIR, (d) postprocessed output using binarized FLAIR, (e) reconstruction error map using ND, (f) postprocessed output using ND error map. Images (b)–(d) and (f) are overlaid on top of FLAIR.
Fig. 6
Fig. 6
Segmentation of ischemic lesion using ND and CND. (a) FLAIR, (b) ND errormap, (c) binarized ND error map, (d) CND error map, (e) binarized CND error map, (f) ground truth, (g) FLAIR, (h) T2, (i) ND error map, (j) binarized ND error map, (k) CND error map, (l) binarized CND error map, (m) ground truth, (n) FLAIR, (o) T2, (p) ND error map, (q) binarized ND error map, (r) CND error map, (s) binarized CND error map, and (t) ground truth. In images (c), (e), (j), (l), (q), and (s), green indicates binarized error map. In images (f), (m), and (t), red indicates ischemic lesion.
Fig. 7
Fig. 7
BraTs dataset. (a) FLAIR, (b) T2, (c) T1, (d) T1c images, (e) associated ground truth, (f) FLAIR at time=1, (g) associated ground truth, (h) FLAIR at time=2, (i) associated ground truth, (j) FLAIR at time=3. (k) associated ground truth, (l) FLAIR at time=4, (m) associated ground truth. In all images, orange indicates edema; yellow indicates nonenhancing tumor; white indicates enhancing tumor; red indicates necrotic region.
Fig. 8
Fig. 8
Axial and sagittal view of ISLES dataset. (a) DWI, (b) FLAIR, (c) T1, (d) T2, (e) ground truth, (f) DWI, (g) FLAIR, (h) T1, (i) T2, and (j) ground truth.
Fig. 9
Fig. 9
(a) Autoencoders—traditional autoencoder; (b) corruption of input by masking noise; (c) denoising autencoder; and (d) deep neural network.
Fig. 10
Fig. 10
Effect of ND based on input patch size (a–g) on ISLES dataset and (i–n) on BraTS dataset. (a) Patch size 11, (b) patch size 15, (c) patch size 21, (d) patch size 31, (e) two layer ND, (f) three layer ND, (g) ground truth, (h) patch size 11, (i) patch size 15, (j) patch size 21, (k) patch size 31, (l) two layer ND, (m) three layer ND, and (n) ground truth. In image (g), red indicates ischemic lesion. In image (n), orange indicates edema, yellow indicates nonenhancing tumor; red indicates necrotic region; and white indicates enhancing tumor.
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