H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images

Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded convolutional neural network design, called H2G-Net, for segmentation of breast cancer region from gigapixel histopathological images. The design involves a detection stage using a patch-wise method, and a refinement stage using a convolutional autoencoder. To validate the design, we conducted an ablation study to assess the impact of selected components in the pipeline on tumor segmentation. Guiding segmentation, using hierarchical sampling and deep heatmap refinement, proved to be beneficial when segmenting the histopathological images. We found a significant improvement when using a refinement network for post-processing the generated tumor segmentation heatmaps. The overall best design achieved a Dice similarity coefficient of 0.933±0.069 on an independent test set of 90 WSIs. The design outperformed single-resolution approaches, such as cluster-guided, patch-wise high-resolution classification using MobileNetV2 (0.872±0.092) and a low-resolution U-Net (0.874±0.128). In addition, the design performed consistently on WSIs across all histological grades and segmentation on a representative × 400 WSI took ~ 58 s, using only the central processing unit. The findings demonstrate the potential of utilizing a refinement network to improve patch-wise predictions. The solution is efficient and does not require overlapping patch inference or ensembling. Furthermore, we showed that deep neural networks can be trained using a random sampling scheme that balances on multiple different labels simultaneously, without the need of storing patches on disk. Future work should involve more efficient patch generation and sampling, as well as improved clustering

H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images

Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded convolutional neural network design, called H2G-Net, for segmentation of breast cancer region from gigapixel histopathological images. The design involves a detection stage using a patch-wise method, and a refinement stage using a convolutional autoencoder. To validate the design, we conducted an ablation study to assess the impact of selected components in the pipeline on tumor segmentation. Guiding segmentation, using hierarchical sampling and deep heatmap refinement, proved to be beneficial when segmenting the histopathological images. We found a significant improvement when using a refinement network for post-processing the generated tumor segmentation heatmaps. The overall best design achieved a Dice similarity coefficient of 0.933±0.069 on an independent test set of 90 WSIs. The design outperformed single-resolution approaches, such as cluster-guided, patch-wise high-resolution classification using MobileNetV2 (0.872±0.092) and a low-resolution U-Net (0.874±0.128). In addition, the design performed consistently on WSIs across all histological grades and segmentation on a representative × 400 WSI took ~ 58 s, using only the central processing unit. The findings demonstrate the potential of utilizing a refinement network to improve patch-wise predictions. The solution is efficient and does not require overlapping patch inference or ensembling. Furthermore, we showed that deep neural networks can be trained using a random sampling scheme that balances on multiple different labels simultaneously, without the need of storing patches on disk. Future work should involve more efficient patch generation and sampling, as well as improved clustering.publishedVersio

Dose distribution in the thyroid gland following radiation therapy of breast cancer-a retrospective study

<p>Abstract</p> <p>Purpose</p> <p>To relate the development of post-treatment hypothyroidism with the dose distribution within the thyroid gland in breast cancer (BC) patients treated with loco-regional radiotherapy (RT).</p> <p>Methods and materials</p> <p>In two groups of BC patients postoperatively irradiated by computer tomography (CT)-based RT, the individual dose distributions in the thyroid gland were compared with each other; Cases developed post-treatment hypothyroidism after multimodal treatment including 4-field RT technique. Matched patients in Controls remained free for hypothyroidism. Based on each patient's dose volume histogram (DVH) the volume percentages of the thyroid absorbing respectively 20, 30, 40 and 50 Gy were then estimated (V20, V30, V40 and V50) together with the individual mean thyroid dose over the whole gland (MeanTotGy). The mean and median thyroid dose for the included patients was about 30 Gy, subsequently the total volume of the thyroid gland (VolTotGy) and the absolute volumes (cm³) receiving respectively < 30 Gy and ≥ 30 Gy were calculated (Vol < 30 and Vol ≥ 30) and analyzed.</p> <p>Results</p> <p>No statistically significant inter-group differences were found between V20, V30, V40 and V50Gy or the median of MeanTotGy. The median VolTotGy in Controls was 2.3 times above VolTotGy in Cases (ρ = 0.003), with large inter-individual variations in both groups. The volume of the thyroid gland receiving < 30 Gy in Controls was almost 2.5 times greater than the comparable figure in Cases.</p> <p>Conclusions</p> <p>We concluded that in patients with small thyroid glands after loco-radiotherapy of BC, the risk of post-treatment hypothyroidism depends on the volume of the thyroid gland.</p

A comparative study of responses in planktonic food web structure and function in contrasting European coastal waters exposed to experimental nutrient addition

We quantify, compare, and generalize responses of experimental nutrient loadings (LN) on planktonic community structure and function in coastal waters. Data were derived from three mesocosm experiments undertaken in Baltic (BAL), Mediterranean (MED), and Norwegian (NOR) coastal waters. A planktonic model with seven functional compartments and 30-32 different carbon flows fit to all three experiments was used as a framework for flow-rate estimation and comparison. Flows were estimated on the basis of time series of measured biomass, some measured flows, and inverse modeling. Biomass and gross uptake rate of carbon of most groups increased linearly with increasing LN in the nutrient input range of 0-1 µmol N L-1 d-1 at all locations. The fate of the gross primary production (GPP) was similar in all systems. Autotrophic biomass varied by two orders of magnitude among locations, with the lowest biomass and response to nutrient addition in MED waters. The variation of GPP among sites was less than one order of magnitude. Mesozooplankton dominated by doliolids (Tunicata), but not those dominated by copepods, presumably exerted efficient control of the autotrophic biomass, thereby buffering responses of autotrophs to high nutrient input. Among the many factors that can modify the responses of autotrophs to nutrients, the time scale over which the enrichment is made and the precise mode of nutrient enrichment are important. We suggest a general concept that may contribute to a scientific basis for understanding and managing coastal eutrophicatio

Testing in the incremental design and development of complex products

Testing is an important aspect of design and development which consumes significant time and resource in many companies. However, it has received less research attention than many other activities in product development, and especially, very few publications report empirical studies of engineering testing. Such studies are needed to establish the importance of testing and inform the development of pragmatic support methods. This paper combines insights from literature study with findings from three empirical studies of testing. The case studies concern incrementally developed complex products in the automotive domain. A description of testing practice as observed in these studies is provided, confirming that testing activities are used for multiple purposes depending on the context, and are intertwined with design from start to finish of the development process, not done after it as many models depict. Descriptive process models are developed to indicate some of the key insights, and opportunities for further research are suggested

State-of-the-art in lean design engineering:a literature review on white collar lean

Lean is usually associated with the ‘operations’ of a manufacturing enterprise; however, there is a growing awareness that these principles may be transferred readily to other functions and sectors. The application to knowledge-based activities such as engineering design is of particular relevance to UK plc. Hence, the purpose of this study has been to establish the state-of-the-art, in terms of the adoption of Lean in new product development, by carrying out a systematic review of the literature. The authors' findings confirm the view that Lean can be applied beneficially away from the factory; that an understanding and definition of value is key to success; that a set-based (or Toyota methodology) approach to design is favoured together with the strong leadership of a chief engineer; and that the successful implementation requires organization-wide changes to systems, practices, and behaviour. On this basis it is felt that this review paper provides a useful platform for further research in this topic