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Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification
Image and Video Processing · Submitted 30 Jun 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim By combining superpixel segmentation and patch classification, we designed a fast and accurate framework for whole-slide cartography that is AI-model agnostic and provides the basis for various medical endpoints.
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The abstract, sentence by sentence
Automatic outlining of different tissue types in digitized histological specimen provides a basis for follow-up analyses and can potentially guide subsequent medical decisions. The immense size of whole-slide-images (WSI), however, poses a challenge in terms of computation time. In this regard, the analysis of non-overlapping patches outperforms pixelwise segmentation approaches, but still leaves room for optimization. Furthermore, the division into patches, regardless of the biological structures they contain, is a drawback due to the loss of local dependencies. We propose to subdivide the WSI into coherent regions prior to classification by grouping visually similar adjacent pixels into superpixels. Afterwards, only a random subset of patches per superpixel is classified and patch labels are combined into a superpixel label. We propose a metric for identifying superpixels with an uncertain classification and evaluate two medical applications, namely tumor area and invasive margin estimation and tumor composition analysis. The algorithm has been developed on 159 hand-annotated WSIs of colon resections and its performance is compared to an analysis without prior segmentation. The algorithm shows an average speed-up of 41% and an increase in accuracy from 93.8% to 95.7%. By assigning a rejection label to uncertain superpixels, we further increase the accuracy by 0.4%. Whilst tumor area estimation shows high concordance to the annotated area, the analysis of tumor composition highlights limitations of our approach. By combining superpixel segmentation and patch classification, we designed a fast and accurate framework for whole-slide cartography that is AI-model agnostic and provides the basis for various medical endpoints.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Automatic outlining of different tissue types in digitized histological specimen provides a basis for follow-up analyses and can potentially guide subsequent medical decisions. | background 1.00 | 0.05 | 0.04 | asserted 0.69 |
| s1 | The immense size of whole-slide-images (WSI), however, poses a challenge in terms of computation time. | background 0.97 | 0.07 | 0.01 | asserted 1.00 |
| s2 | In this regard, the analysis of non-overlapping patches outperforms pixelwise segmentation approaches, but still leaves room for optimization. | background 0.84 | 0.12 | 0.02 | asserted 0.97 |
| s3 | Furthermore, the division into patches, regardless of the biological structures they contain, is a drawback due to the loss of local dependencies. | background 0.69 | 0.08 | 0.02 | asserted 1.00 |
| s4 | We propose to subdivide the WSI into coherent regions prior to classification by grouping visually similar adjacent pixels into superpixels. | contribution 0.57 | 0.83 | 0.06 | asserted 0.91 |
| s5 | Afterwards, only a random subset of patches per superpixel is classified and patch labels are combined into a superpixel label. | method 0.99 | 0.87 | 0.04 | asserted 0.95 |
| s6 | We propose a metric for identifying superpixels with an uncertain classification and evaluate two medical applications, namely tumor area and invasive margin estimation and tumor composition analysis. | contribution 0.87 | 0.96 | 0.08 | asserted 0.99 |
| s7 | The algorithm has been developed on 159 hand-annotated WSIs of colon resections and its performance is compared to an analysis without prior segmentation. | method 0.98 | 0.95 | 0.04 | asserted 0.98 |
| s8 | The algorithm shows an average speed-up of 41% and an increase in accuracy from 93.8% to 95.7%. | result 1.00 | 0.96 | 0.22 | asserted 1.00 |
| s9 | By assigning a rejection label to uncertain superpixels, we further increase the accuracy by 0.4%. | result 0.98 | 0.96 | 0.06 | asserted 1.00 |
| s10 | Whilst tumor area estimation shows high concordance to the annotated area, the analysis of tumor composition highlights limitations of our approach. | limitation 0.70 | 0.94 | 0.10 | asserted 1.00 |
| s11 | By combining superpixel segmentation and patch classification, we designed a fast and accurate framework for whole-slide cartography that is AI-model agnostic and provides the basis for various medical endpoints. | contribution 0.90 | 0.92 | 0.89 | asserted 1.00 |
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Original abstract
Automatic outlining of different tissue types in digitized histological specimen provides a basis for follow-up analyses and can potentially guide subsequent medical decisions. The immense size of whole-slide-images (WSI), however, poses a challenge in terms of computation time. In this regard, the analysis of non-overlapping patches outperforms pixelwise segmentation approaches, but still leaves room for optimization. Furthermore, the division into patches, regardless of the biological structures they contain, is a drawback due to the loss of local dependencies. We propose to subdivide the WSI into coherent regions prior to classification by grouping visually similar adjacent pixels into superpixels. Afterwards, only a random subset of patches per superpixel is classified and patch labels are combined into a superpixel label. We propose a metric for identifying superpixels with an uncertain classification and evaluate two medical applications, namely tumor area and invasive margin estimation and tumor composition analysis. The algorithm has been developed on 159 hand-annotated WSIs of colon resections and its performance is compared to an analysis without prior segmentation. The algorithm shows an average speed-up of 41% and an increase in accuracy from 93.8% to 95.7%. By assigning a rejection label to uncertain superpixels, we further increase the accuracy by 0.4%. Whilst tumor area estimation shows high concordance to the annotated area, the analysis of tumor composition highlights limitations of our approach. By combining superpixel segmentation and patch classification, we designed a fast and accurate framework for whole-slide cartography that is AI-model agnostic and provides the basis for various medical endpoints.
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