A hybrid deep learning approach for gland segmentation in prostate histopathological images

Salvi, Massimo; Bosco, Martino; Molinaro, Luca; Gambella, Alessandro; Papotti, Mauro; Acharya, U. Rajendra; Molinari, Filippo

doi:10.1016/j.artmed.2021.102076

Cited by 36 publications

(19 citation statements)

References 34 publications

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“…This means that the least represented class (prostate gland) will have a greater contribution than the more represented one (background) during the weight update. This is performed following the same approach of our previous work [ 35 ]. Dice loss: the network loss function is calculated as 1-DSC, where DSC is the dice score computed between the manual annotation and the network prediction.…”

Section: Methodsmentioning

confidence: 99%

Integration of Deep Learning and Active Shape Models for More Accurate Prostate Segmentation in 3D MR Images

et al. 2022

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Magnetic resonance imaging (MRI) has a growing role in the clinical workup of prostate cancer. However, manual three-dimensional (3D) segmentation of the prostate is a laborious and time-consuming task. In this scenario, the use of automated algorithms for prostate segmentation allows us to bypass the huge workload of physicians. In this work, we propose a fully automated hybrid approach for prostate gland segmentation in MR images using an initial segmentation of prostate volumes using a custom-made 3D deep network (VNet-T2), followed by refinement using an Active Shape Model (ASM). While the deep network focuses on three-dimensional spatial coherence of the shape, the ASM relies on local image information and this joint effort allows for improved segmentation of the organ contours. Our method is developed and tested on a dataset composed of T2-weighted (T2w) MRI prostatic volumes of 60 male patients. In the test set, the proposed method shows excellent segmentation performance, achieving a mean dice score and Hausdorff distance of 0.851 and 7.55 mm, respectively. In the future, this algorithm could serve as an enabling technology for the development of computer-aided systems for prostate cancer characterization in MR imaging.

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Section: Methodsmentioning

confidence: 99%

Integration of Deep Learning and Active Shape Models for More Accurate Prostate Segmentation in 3D MR Images

et al. 2022

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“…The automatic segmentation of tissues in histology images has been explored by many studies [ 86 , 87 ]. Traditional tissue segmentation methods usually relied on the extraction of handcrafted features, the design of conventional classifiers [ 88 ].…”

Section: Pathology Image Segmentationmentioning

confidence: 99%

Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications

Cheng

Huang

et al. 2022

Cancers

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With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.

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“…Moreover, we compared the Mask R-CNN model's performance with other literature methods on a publicly available gland segmentation dataset [23] (Table 2). The Mask R-CNN model slightly outperformed deep learning-based segmentation methods.…”

Section: Mask R-cnn Model Successfully Segmented Prostate Glandsmentioning

confidence: 99%

“…Performances of different methods in prostate gland segmentation in terms of pixel-based metrics. The performances were on the test dataset of Salvi et al[23]. Note that accuracy values were the balanced accuracy values as in Salvi et al[23], and all the performance values except the one for the Mask R-CNN model were collected from Salvi et al[23].…”

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confidence: 99%

“…The performances were on the test dataset of Salvi et al[23]. Note that accuracy values were the balanced accuracy values as in Salvi et al[23], and all the performance values except the one for the Mask R-CNN model were collected from Salvi et al[23]. †, ‡ 0.9410 ± 0.0010 0.9002 ± 0.0026 0.9468 ± 0.0011 0.9229 ± 0.0015 * Traditional image processing or machine learning-based methods.…”

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confidence: 99%

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An AI-assisted Tool For Efficient Prostate Cancer Diagnosis

Öner

Giron

et al. 2022

Preprint

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Pathologists diagnose prostate cancer by core needle biopsy. For low-grade and low-volume cases, the pathologists look for the few malignant glands out of hundreds within a core. They may miss the few malignant glands, resulting in repeat biopsies or missed therapeutic opportunities. This study developed a multi-resolution deep learning pipeline detecting malignant glands in core needle biopsies to help pathologists effectively and accurately diagnose prostate cancer in low-grade and low-volume cases. The pipeline consisted of two stages: the gland segmentation model detected the glands within the sections and the multi-resolution model classified each detected gland into benign vs. malignant. Analyzing a gland at multiple resolutions provided the classification model to exploit both morphology information (of nuclei and glands) and neighborhood information (for architectural patterns), important in prostate gland classification. We developed and tested our pipeline on the slides of a local cohort of 99 patients in Singapore. The images were made publicly available, becoming the first digital histopathology dataset of prostatic carcinoma patients of Asian ancestry. Our pipeline successfully classified the core needle biopsy parts (81 parts: 50 benign and 31 malignant) into benign vs. malignant. It achieved an AUROC value of 0.997 (95% CI: 0.987 - 1.000). Moreover, it produced heatmaps highlighting the malignancy of each gland in core needle biopsies. Hence, our pipeline can effectively assist pathologists in core needle biopsy analysis.

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A hybrid deep learning approach for gland segmentation in prostate histopathological images

Cited by 36 publications

References 34 publications

Integration of Deep Learning and Active Shape Models for More Accurate Prostate Segmentation in 3D MR Images

Integration of Deep Learning and Active Shape Models for More Accurate Prostate Segmentation in 3D MR Images

Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications

An AI-assisted Tool For Efficient Prostate Cancer Diagnosis

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