Dataset Information

Semantic segmentation to identify bladder layers from H&E Images.

ABSTRACT:

Background

Identification of bladder layers is a necessary prerequisite to bladder cancer diagnosis and prognosis. We present a method of multi-class image segmentation, which recognizes urothelium, lamina propria, muscularis propria, and muscularis mucosa layers as well as regions of red blood cells, cauterized tissue, and inflamed tissue from images of hematoxylin and eosin stained slides of bladder biopsies.

Methods

Segmentation is carried out using a U-Net architecture. The number of layers was either, eight, ten, or twelve and combined with a weight initializers of He uniform, He normal, Glorot uniform, and Glorot normal. The most optimal of these parameters was found by through a seven-fold training, validation, and testing of a dataset of 39 whole slide images of T1 bladder biopsies.

Results

The most optimal model was a twelve layer U-net using He normal initializer. Initial visual evaluation by an experienced pathologist on an independent set of 15 slides segmented by our method yielded an average score of 8.93 ± 0.6 out of 10 for segmentation accuracy. It took only 23?min for the pathologist to review 15 slides (1.53?min/slide) with the computer annotations. To assess the generalizability of the proposed model, we acquired an additional independent set of 53 whole slide images and segmented them using our method. Visual examination by a different experienced pathologist yielded an average score of 8.87 ± 0.63 out of 10 for segmentation accuracy.

Conclusions

Our preliminary findings suggest that predictions of our model can minimize the time needed by pathologists to annotate slides. Moreover, the method has the potential to identify the bladder layers accurately. Further development can assist the pathologist with the diagnosis of T1 bladder cancer.

SUBMITTER: Niazi MKK

PROVIDER: S-EPMC7364471 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Publications

Semantic segmentation to identify bladder layers from H&E Images.

Niazi Muhammad Khalid Khan MKK Yazgan Enes E Tavolara Thomas E TE Li Wencheng W Lee Cheryl T CT Parwani Anil A Gurcan Metin N MN

Diagnostic pathology 20200716 1

<h4>Background</h4>Identification of bladder layers is a necessary prerequisite to bladder cancer diagnosis and prognosis. We present a method of multi-class image segmentation, which recognizes urothelium, lamina propria, muscularis propria, and muscularis mucosa layers as well as regions of red blood cells, cauterized tissue, and inflamed tissue from images of hematoxylin and eosin stained slides of bladder biopsies.<h4>Methods</h4>Segmentation is carried out using a U-Net architecture. The nu ...[more]

PMID: 32677978

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Project description:AimThe main objective of this work is to propose an efficient segmentation model for accurate and robust lung segmentation from computed tomography (CT) images, even when the lung contains abnormalities such as juxtapleural nodules, cavities, and consolidation.MethodologyA novel deep learning-based segmentation model, pyramid-dilated dense U-Net (PDD-U-Net), is proposed to directly segment lung regions from the whole CT image. The model is integrated with pyramid-dilated convolution blocks to capture and preserve multi-resolution spatial features effectively. In addition, shallow and deeper stream features are embedded in the nested U-Net structure at the decoder side to enhance the segmented output. The effect of three loss functions is investigated in this paper, as the medical image analysis method requires precise boundaries. The proposed PDD-U-Net model with shape-aware loss function is tested on the lung CT segmentation challenge (LCTSC) dataset with standard lung CT images and the lung image database consortium-image database resource initiative (LIDC-IDRI) dataset containing both typical and pathological lung CT images.ResultsThe performance of the proposed method is evaluated using Intersection over Union, dice coefficient, precision, recall, and average Hausdorff distance metrics. Segmentation results showed that the proposed PDD-U-Net model outperformed other segmentation methods and achieved a 0.983 dice coefficient for the LIDC-IDRI dataset and a 0.994 dice coefficient for the LCTSC dataset.ConclusionsThe proposed PDD-U-Net model with shape-aware loss function is an effective and accurate method for lung segmentation from CT images, even in the presence of abnormalities such as cavities, consolidation, and nodules. The model's integration of pyramid-dilated convolution blocks and nested U-Net structure at the decoder side, along with shape-aware loss function, contributed to its high segmentation accuracy. This method could have significant implications for the computer-aided diagnosis system, allowing for quick and accurate analysis of lung regions.

Project description:IntroductionBladder magnetic resonance imaging (MRI) has been recently integrated in the diagnosis pathway of bladder cancer. However, automatic recognition of suspicious lesions is still challenging. Thus, development of a solution for proper delimitation of the tumor and its separation from the healthy tissue is of primordial importance. As a solution to this unmet medical need, we aimed to develop an artificial intelligence-based decision support system, which automatically segments the bladder wall and the tumor as well as any suspect area from the 3D MRI images.MaterialsWe retrospectively assessed all patients diagnosed with bladder cancer, who underwent MRI at our department (n=33). All examinations were performed using a 1.5 Tesla MRI scanner. All images were reviewed by two radiologists, who performed manual segmentation of the bladder wall and all lesions. First, the performance of our fully automated end-to-end segmentation model based on a 3D U-Net architecture (by considering various depths of 4, 5 or 6 blocks) trained in two data augmentation scenarios (on 5 and 10 augmentation datasets per original data, respectively) was tested. Second, two learning setups were analyzed by training the segmentation algorithm with 7 and 14 MRI original volumes, respectively.ResultsWe obtained a Dice-based performance over 0.878 for automatic segmentation of bladder wall and tumors, as compared to manual segmentation. A larger training dataset using 10 augmentations for 7 patients could further improve the results of the U-Net-5 model (0.902 Dice coefficient at image level). This model performed best in terms of automated segmentation of bladder, as compared to U-Net-4 and U-Net-6. However, in this case increased time for learning was needed as compared to U-Net-4. We observed that an extended dataset for training led to significantly improved segmentation of the bladder wall, but not of the tumor.ConclusionWe developed an intelligent system for bladder tumors automated diagnostic, that uses a deep learning model to segment both the bladder wall and the tumor. As a conclusion, low complexity networks, with less than five-layers U-Net architecture are feasible and show good performance for automatic 3D MRI image segmentation in patients with bladder tumors.

Dataset Information

Semantic segmentation to identify bladder layers from H&E Images.

Background

Methods

Results

Conclusions

Publications

Semantic segmentation to identify bladder layers from H&E Images.

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