MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge

Verma, Ruchika; Kumar, Neeraj; Patil, Abhijeet; Kurian, Nikhil Cherian; Rane, Swapnil; Graham, Simon; Vu, Quoc Dang; Zwager, Mieke C.; Raza, Shan E. Ahmed; Rajpoot, Nasir M.; Wu, Xiyi; Chen, Huai; Huang, Yalin; Wang, Lisheng; Jung, Hyun; Brown, Gavin; Liu, Yanling; Liu, Shuolin; Jahromi, Seyed Alireza Fatemi; Khani, Ali Asghar; Montahaei, Ehsan; Baghshah, Mahdieh Soleymani; Behroozi, Hamid; Semkin, P. Yu.; Rassadin, Alexandr G.; Dutande, Prasad; Lodaya, Romil; Baid, Ujjwal; Baheti, Bhakti; Talbar, Sanjay N.; Mahbod, Amirreza; Ecker, Rupert; Ellinger, Isabella; Luo, Zhipeng; Dong, Bin; Xu, Z. Y.; Yao, Yuehan; Lv, Shuai; Feng, Ming; Xu, Kele; Zunair, Hasib; Hamza, A. Ben; Smiley, Steven; Yin, Tang-Kai; Fang, Qi-Rui; Srivastava, Shikhar; Mahapatra, Dwarikanath; Trnavska, Lubomira; Zhang, Hanyun; Narayanan, Priya; Law, Justin J.; Yuan, Yinyin; Tejomay, Abhiroop; Mitkari, Aditya; Koka, Dinesh; Ramachandra, Vikas; Kini, Lata; Sethi, Amit

doi:10.1109/tmi.2021.3085712

Cited by 93 publications

(47 citation statements)

References 55 publications

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“…The state-of-the-art deep-learning-based segmentation models have achieved excellent performance in nuclear segmentation [22,23]. As illustrated in Figure 5, QuPath with the default setting can segment the cell boundary successfully in most cases; however, fragile contours and false positives occurred frequently when cells were crowded, vesicular, or with atypical chromatin patterns.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas

Yu¹,

Wang

et al. 2021

Cancers

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The aim of this study was to investigate the feasibility of using machine learning techniques based on morphological features in classifying two subtypes of primary intestinal T-cell lymphomas (PITLs) defined according to the WHO criteria: monomorphic epitheliotropic intestinal T-cell lymphoma (MEITL) versus intestinal T-cell lymphoma, not otherwise specified (ITCL-NOS), which is considered a major challenge for pathological diagnosis. A total of 40 histopathological whole-slide images (WSIs) from 40 surgically resected PITL cases were used as the dataset for model training and testing. A deep neural network was trained to detect and segment the nuclei of lymphocytes. Quantitative nuclear morphometrics were further computed from these predicted contours. A decision-tree-based machine learning algorithm, XGBoost, was then trained to classify PITL cases into two disease subtypes using these nuclear morphometric features. The deep neural network achieved an average precision of 0.881 in the cell segmentation work. In terms of classifying MEITL versus ITCL-NOS, the XGBoost model achieved an area under receiver operating characteristic curve (AUC) of 0.966. Our research demonstrated an accurate, human-interpretable approach to using machine learning algorithms for reducing the high dimensionality of image features and classifying T cell lymphomas that present challenges in morphologic diagnosis. The quantitative nuclear morphometric features may lead to further discoveries concerning the relationship between cellular phenotype and disease status.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas

Yu¹,

Wang

et al. 2021

Cancers

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“…Fuzzy or unclear borders between touching nuclei, folded tissues and other acquisition artefacts, manual annotation errors in the nuclei borders, and sensitivity loss of the annotators due to fatigue are some parameters that can cause interobserver and intra-observer variability issues. These problems can be partially resolved by removing vague areas in the manual segmentation masks [9], but this requires extra supervision and time.…”

Section: Discussionmentioning

confidence: 99%

CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Images

Mahbod,

Schaefer,

Bancher

et al. 2021

Preprint

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Nuclei instance segmentation plays an important role in the analysis of Hematoxylin and Eosin (H&E)-stained images. While supervised deep learning (DL)-based approaches represent the state-of-the-art in automatic nuclei instance segmentation, annotated datasets are required to train these models. There are two main types of tissue processing protocols, namely formalin-fixed paraffin-embedded samples (FFPE) and frozen tissue samples (FS). Although FFPE-derived H&E stained tissue sections are the most widely used samples, H&E staining on frozen sections derived from FS samples is a relevant method in intra-operative surgical sessions as it can be performed fast. Due to differences in the protocols of these two types of samples, the derived images and in particular the nuclei appearance may be different in the acquired whole slide images. Analysis of FS-derived H&E stained images can be more challenging as rapid preparation, staining, and scanning of FS sections may lead to deterioration in image quality.In this paper, we introduce CryoNuSeg, the first fully annotated FS-derived cryosectioned and H&E-stained nuclei instance segmentation dataset. The dataset contains images from 10 human organs that were not exploited in other publicly available datasets, and is provided with three manual mark-ups to allow measuring intra-observer and interobserver variability. Moreover, we investigate the effects of tissue fixation/embedding protocol (i.e., FS or FFPE) on the automatic nuclei instance segmentation performance of one of the state-of-the-art DL approaches. We also create a baseline segmentation benchmark for the dataset that can be used in future research.A step-by-step guide to generate the dataset as well as the full dataset and other detailed information are made available to fellow researchers at https://github.com/ masih4/CryoNuSeg.

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“…Adam optimizer is adopted, and the total training epochs 50. The weighted panoptic quality (wPQ) used in the MoNuSAC challenge [17] is adopted for evaluation in which the weight of each class is 1. The wPQ of Micro-Net is up to 0.5, which is an acceptable performance for pRCC nuclei segmentation and classification.…”

Section: Implementation Details Nuclei Segmentation and Classificationmentioning

confidence: 99%

Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image

Gao¹,

Hong²,

Zhang³

et al. 2021

Preprint

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Histological subtype of papillary (p) renal cell carcinoma (RCC), type 1 vs. type 2, is an essential prognostic factor. The two subtypes of pRCC have a similar pattern, i.e., the papillary architecture, yet some subtle differences, including cellular and cell-layer level patterns. However, the cellular and cell-layer level patterns almost cannot be captured by existing CNN-based models in large-size histopathological images, which brings obstacles to directly applying these models to such a fine-grained classification task. This paper proposes a novel instance-based Vision Transformer (i-ViT) to learn robust representations of histopathological images for the pRCC subtyping task by extracting finer features from instance patches (by cropping around segmented nuclei and assigning predicted grades). The proposed i-ViT takes top-K instances as input and aggregates them for capturing both the cellular and cell-layer level patterns by a position-embedding layer, a gradeembedding layer, and a multi-head multi-layer self-attention module. To evaluate the performance of the proposed framework, experienced pathologists are invited to selected 1162 regions of interest from 171 whole slide images of type 1 and type 2 pRCC. Experimental results show that the proposed method achieves better performance than existing CNN-based models with a significant margin.

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MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge

Cited by 93 publications

References 55 publications

Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas

Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas

CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Images

Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image

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