Methods for Segmentation and Classification of Digital Microscopy Tissue Images

Vu, Quoc Dang; Graham, Simon J.; To, Minh Nguyen Nhat; Shaban, Muhammad; Qaiser, Talha; Koohbanani, Navid Alemi; Khurram, Syed Ali; Kurç, Tahsin; Farahani, Keyvan; Zhao, Tianhao; Gupta, Rajarsi; Kwak, Jin Tae; Rajpoot, Nasir M.; Saltz, Joel

doi:10.48550/arxiv.1810.13230

Cited by 4 publications

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“…For this experiment, because we do not have the classification labels for all datasets, we perform instance segmentation without classification. This enables us to From left to right: Kumar [27]; CoNSeP; CPM-15; CPM-17 [30] and TNBC [31]. The different colours of nuclear contours highlight individual instances.…”

Section: Comparative Analysis Of Segmentation Methodsmentioning

confidence: 99%

“…We compared our proposed model to recent segmentation approaches used in computer vision [21], [44], [32], medical imaging [22] and also to methods specifically tuned for the task of nuclear segmentation [25], [23], [31], [29], [30]. We also compared the performance of our model to two open source software applications: Cell Profiler [42] and QuPath [43].…”

Section: Comparative Analysis Of Segmentation Methodsmentioning

confidence: 99%

“…[29] proposed CIA-Net, that utilises a multi-level information aggregation module between two task-specific decoders, where each decoder segments either the nuclei or the contours. A Deep Residual Aggregation Network (DRAN) was proposed by [30] that uses a multi-scale strategy, incorporating both the nuclei and nuclear contours to accurately segment nuclei.…”

Section: Related Work a Nuclear Instance Segmentationmentioning

confidence: 99%

“…• Separate the nuclei from the background • Detect individual nuclear instances • Segment each detected instance In the current literature, two evaluation metrics have been mainly adopted to quantitatively measure the performance of nuclear instance segmentation: 1) Ensemble Dice (DICE2) [30], and 2) Aggregated Jaccard Index (AJI) [27]. Given the ground truth X and prediction Y , DICE2 computes and aggregates DICE per nucleus, where Dice coefficient (DICE) is defined as 2×(X∩Y )/(|X|+|Y |) and AJI computes the ratio of an aggregated intersection cardinality and an aggregated union cardinality between X and Y .…”

Section: Evaluation Metrics a Nuclear Instance Segmentation Evaluationmentioning

confidence: 99%

“…A full summary for each of them is provided in Table II. Five of these datasets are used to evaluate the instance segmentation performance which we refer to as: CoNSeP; Kumar [27]; CPM-15; CPM-17 [30] and TNBC [31]. Example images from each of the five datasets can be seen in Fig.…”

Section: A Datasetsmentioning

confidence: 99%

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Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

Graham

Raza

et al. 2019

Medical Image Analysis

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Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow. The development of automated methods for nuclear segmentation and classification enables the quantitative analysis of tens of thousands of nuclei within a whole-slide pathology image, opening up possibilities of further analysis of large-scale nuclear morphometry. However, automated nuclear segmentation and classification is faced with a major challenge in that there are several different types of nuclei, some of them exhibiting large intra-class variability such as the nuclei of tumour cells. Additionally, some of the nuclei are often clustered together. To address these challenges, we present a novel convolutional neural network for simultaneous nuclear segmentation and classification that leverages the instance-rich information encoded within the vertical and horizontal distances of nuclear pixels to their centres of mass. These distances are then utilised to separate clustered nuclei, resulting in an accurate segmentation, particularly in areas with overlapping instances. Then, for each segmented instance the network predicts the type of nucleus via a devoted up-sampling branch. We demonstrate state-of-the-art performance compared to other methods on multiple independent multi-tissue histology image datasets. As part of this work, we introduce a new dataset of Haematoxylin & Eosin stained colorectal adenocarcinoma image tiles, containing 24,319 exhaustively annotated nuclei with associated class labels.

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Section: Comparative Analysis Of Segmentation Methodsmentioning

confidence: 99%

Section: Comparative Analysis Of Segmentation Methodsmentioning

confidence: 99%

Section: Related Work a Nuclear Instance Segmentationmentioning

confidence: 99%

Section: Evaluation Metrics a Nuclear Instance Segmentation Evaluationmentioning

confidence: 99%

Section: A Datasetsmentioning

confidence: 99%

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Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

Graham

Raza

et al. 2019

Medical Image Analysis

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775

601

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A survey on recent trends in deep learning for nucleus segmentation from histopathology images

et al. 2023

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Nucleus segmentation is an imperative step in the qualitative study of imaging datasets, considered as an intricate task in histopathology image analysis. Segmenting a nucleus is an important part of diagnosing, staging, and grading cancer, but overlapping regions make it hard to separate and tell apart independent nuclei. Deep Learning is swiftly paving its way in the arena of nucleus segmentation, attracting quite a few researchers with its numerous published research articles indicating its efficacy in the field. This paper presents a systematic survey on nucleus segmentation using deep learning in the last five years (2017–2021), highlighting various segmentation models (U-Net, SCPP-Net, Sharp U-Net, and LiverNet) and exploring their similarities, strengths, datasets utilized, and unfolding research areas.

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Image Analysis of Nuclei Histopathology Using Deep Learning: A Review of Segmentation, Detection, and Classification

Kadaskar,

Patil

2023

SN COMPUT. SCI.

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Methods for Segmentation and Classification of Digital Microscopy Tissue Images

Cited by 4 publications

References 0 publications

Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

A survey on recent trends in deep learning for nucleus segmentation from histopathology images

Image Analysis of Nuclei Histopathology Using Deep Learning: A Review of Segmentation, Detection, and Classification

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