A Robust Method for Nuclei Segmentation of H&amp;E Stained Histopathology Images

Lal, Shyam; Desouza, R. C. M.; Maneesh, M; Kanfade, Anirudh; Kumar, Aman; Perayil, Gokul; Alabhya, Kumar; Chanchal, Amit Kumar; Kini, Jyoti R.

doi:10.1109/spin48934.2020.9070874

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…We compared our proposed model with five other CNN models which is a benchmark in the field of biomedical image segmentation. We expressed our simulation results in terms of F1-Score used by Lal S et al and Aatresh A A et al in [2,21] and Aggregated Jaccard Index (AJI) used by Naylor P et al in [25]. By calculating the harmonic mean of precision and recall, the F1 score is calculated and is the most preferred method to measure the retrieved information.…”

Section: Resultsmentioning

confidence: 99%

“…For the segmentation of microscopic, MR, and CT images an encoder-decoder architecture by Zhou S et al in [39], linked meaningful connections to precisely locate the complex boundaries. For the segmentation of nuclei in pathology images, Lal S et al model [21], consists of adaptive color deconvolution, multiscale thresholding followed by morphological operations, and other post-processing steps. For the segmentation of medical images, a novel loss function by Karimi D et al in [16], estimated Hausdorff distance using the morphological operation method, distance transformation method, and by circularly convoluted kernels of different radius.…”

Section: Related Workmentioning

confidence: 99%

“…Followings are the significant controlling parameters that we fine-tuned: (a) Learning rate= 0.0001 for training of all the model with three datasets (b) Weight decay constant, we have selected learning rate and weight decay constant based on trial and error (c) Batch size=4, Larger batch size allow us to parallelize computations to a greater degree but lead to poor generalization (D) The size of the convolution filter used as per available resources. We have considered F1-score used by Lal S et al and Aatresh A A et al in [2,21], and Aggregated Jaccard Index (AJI) used by Naylor P et al in [25], the total number of parameters, and FLOPs (Floating point operations), which are mostly preferred performance measure metrics for comparison of nuclei segmentation.…”

Section: Training and Implementationmentioning

confidence: 99%

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Deep structured residual encoder-decoder network with a novel loss function for nuclei segmentation of kidney and breast histopathology images

Chanchal

Lal

Kini

2022

Multimed Tools Appl

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To improve the process of diagnosis and treatment of cancer disease, automatic segmentation of haematoxylin and eosin (H & E) stained cell nuclei from histopathology images is the first step in digital pathology. The proposed deep structured residual encoder-decoder network (DSREDN) focuses on two aspects: first, it effectively utilized residual connections throughout the network and provides a wide and deep encoder-decoder path, which results to capture relevant context and more localized features. Second, vanished boundary of detected nuclei is addressed by proposing an efficient loss function that better train our proposed model and reduces the false prediction which is undesirable especially in healthcare applications. The proposed architecture experimented on three different publicly available H&E stained histopathological datasets namely: (I) Kidney (RCC) (II) Triple Negative Breast Cancer (TNBC) (III) MoNuSeg-2018. We have considered F1-score, Aggregated Jaccard Index (AJI), the total number of parameters, and FLOPs (Floating point operations), which are mostly preferred performance measure metrics for comparison of nuclei segmentation. The evaluated score of nuclei segmentation indicated that the proposed architecture achieved a considerable margin over five state-of-the-art deep learning models on three different histopathology datasets. Visual segmentation results show that the proposed DSREDN model accurately segment the nuclear regions than those of the state-of-the-art methods.

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Section: Resultsmentioning

confidence: 99%