Evaluation of deep learning training strategies for the classification of bone marrow cell images

BACKGROUND AND OBJECTIVE The classification of bone marrow (BM) cells by light microscopy is an important cornerstone of hematological diagnosis, performed thousands of times a day by highly trained specialists in laboratories worldwide. As the manual evaluation of blood or BM smears is very time-consuming and prone to inter-observer variation, new reliable automated systems are needed. METHODS We aim to improve the automatic classification performance of hematological cell types. Therefore, we evaluate four state-of-the-art Convolutional Neural Network (CNN) architectures on a dataset of 171,374 microscopic cytological single-cell images obtained from BM smears from 945 patients diagnosed with a variety of hematological diseases. We further evaluate the effect of an in-domain vs. out-of-domain pre-training, and assess whether class activation maps provide human-interpretable explanations for the models' predictions. RESULTS The best performing pre-trained model (Regnet_y_32gf) yields a mean precision, recall, and F1 scores of 0.787±0.060, 0.755±0.061, and 0.762±0.050, respectively. This is a 53.5% improvement in precision and 7.3% improvement in recall over previous results with CNNs (ResNeXt-50) that were trained from scratch. The out-of-domain pre-training apparently yields general feature extractors/filters that apply very well to the BM cell classification use case. The class activation maps on cell types with characteristic morphological features were found to be consistent with the explanations of a human domain expert. For example, the Auer rods in the cytoplasm were the predictive cellular feature for correctly classified images of faggot cells. CONCLUSIONS Our study provides data that can help hematology laboratories to choose the optimal training strategy for blood cell classification deep learning models to improve computer-assisted blood and bone marrow cell identification. It also highlights the need for more specific training data, i.e. images of difficult-to-classify classes, including cells labeled with disease information

Evaluation of deep learning training strategies for the classification of bone marrow cell images

Background and Objective: The classification of bone marrow (BM) cells by light mi- croscopy is an important cornerstone of hematological diagnosis, performed thousands of times a day by highly trained specialists in laboratories worldwide. As the manual evaluation of blood or BM smears is very time-consuming and prone to inter-observer variation, new reliable automated systems are needed. Methods: We aim to improve the automatic classification performance of hematolog- ical cell types. Therefore, we evaluate four state-of-the-art Convolutional Neural Net- work (CNN) architectures on a dataset of 171, 374 microscopic cytological single-cell images obtained from BM smears from 945 patients diagnosed with a variety of hema- tological diseases. We further evaluate the effect of an in-domain vs. out-of-domain pre-training, and assess whether class activation maps provide human-interpretable ex- planations for the models’ predictions. Results: The best performing pre-trained model (Regnet y 32gf) yields a mean pre- cision, recall, and F1 scores of 0.787 ± 0.060, 0.755 ± 0.061, and 0.762 ± 0.050, re- spectively. This is a 53.5% improvement in precision and 7.3% improvement in recall over previous results with CNNs (ResNeXt-50) that were trained from scratch. The out-of-domain pre-training apparently yields general feature extractors/filters that ap- ply very well to the BM cell classification use case. The class activation maps on cell types with characteristic morphological features were found to be consistent with the explanations of a human domain expert. For example, the Auer rods in the cytoplasm were the predictive cellular feature for correctly classified images of faggot cells. Conclusions: Our study provides data that can help hematology laboratories to choose the optimal training strategy for blood cell classification deep learning mod- els to improve computer-assisted blood and bone marrow cell identification. It also highlights the need for more specific training data, i.e. images of difficult-to-classify classes, including cells labeled with disease information

Towards a national strategy for digital pathology in Switzerland.

Precision medicine is entering a new era of digital diagnostics; the availability of integrated digital pathology (DP) and structured clinical datasets has the potential to become a key catalyst for biomedical research, education and business development. In Europe, national programs for sharing of this data will be crucial for the development, testing, and validation of machine learning-enabled tools supporting clinical decision-making. Here, the Swiss Digital Pathology Consortium (SDiPath) discusses the creation of a Swiss Digital Pathology Infrastructure (SDPI), which aims to develop a unified national DP network bringing together the Swiss Personalized Health Network (SPHN) with Swiss university hospitals and subsequent inclusion of cantonal and private institutions. This effort builds on existing developments for the national implementation of structured pathology reporting. Opening this national infrastructure and data to international researchers in a sequential rollout phase can enable the large-scale integration of health data and pooling of resources for research purposes and clinical trials. Therefore, the concept of a SDPI directly synergizes with the priorities of the European Commission communication on the digital transformation of healthcare on an international level, and with the aims of the Swiss State Secretariat for Economic Affairs (SECO) for advancing research and innovation in the digitalization domain. SDPI directly addresses the needs of existing national and international research programs in neoplastic and non-neoplastic diseases by providing unprecedented access to well-curated clinicopathological datasets for the development and implementation of novel integrative methods for analysis of clinical outcomes and treatment response. In conclusion, a SDPI would facilitate and strengthen inter-institutional collaboration in technology, clinical development, business and research at a national and international scale, promoting improved patient care via precision medicine

Suppression of intratumoral CCL22 by type I interferon inhibits migration of regulatory T cells and blocks cancer progression

The chemokine CCL22 is abundantly expressed in many types of cancer and is instrumental for intratumoral recruitment of regulatory T cells (Treg), an important subset of immunosuppressive and tumor-promoting lymphocytes. In this study, we offer evidence for a generalized strategy to blunt Treg activity that can limit immune escape and promote tumor rejection. Activation of innate immunity with Toll-like receptor (TLR) or RIG-I-like receptor (RLR) ligands prevented accumulation of Treg in tumors by blocking their immigration. Mechanistic investigations indicated Treg blockade was a consequence of reduced intratumoral CCL22 levels caused by type I interferon. Notably, stable expression of CCL22 abrogated the antitumor effects of treatment with RLR or TLR ligands. Taken together, our findings argue that type I interferon blocks the Treg-attracting chemokine CCL22 and thus helps limit the recruitment of Treg to tumors, a finding with implications for cancer immunotherapy

Molecular evolutionary trends and feeding ecology diversification in the Hemiptera, anchored by the milkweed bug genome.

BACKGROUND: The Hemiptera (aphids, cicadas, and true bugs) are a key insect order, with high diversity for feeding ecology and excellent experimental tractability for molecular genetics. Building upon recent sequencing of hemipteran pests such as phloem-feeding aphids and blood-feeding bed bugs, we present the genome sequence and comparative analyses centered on the milkweed bug Oncopeltus fasciatus, a seed feeder of the family Lygaeidae. RESULTS: The 926-Mb Oncopeltus genome is well represented by the current assembly and official gene set. We use our genomic and RNA-seq data not only to characterize the protein-coding gene repertoire and perform isoform-specific RNAi, but also to elucidate patterns of molecular evolution and physiology. We find ongoing, lineage-specific expansion and diversification of repressive C2H2 zinc finger proteins. The discovery of intron gain and turnover specific to the Hemiptera also prompted the evaluation of lineage and genome size as predictors of gene structure evolution. Furthermore, we identify enzymatic gains and losses that correlate with feeding biology, particularly for reductions associated with derived, fluid nutrition feeding. CONCLUSIONS: With the milkweed bug, we now have a critical mass of sequenced species for a hemimetabolous insect order and close outgroup to the Holometabola, substantially improving the diversity of insect genomics. We thereby define commonalities among the Hemiptera and delve into how hemipteran genomes reflect distinct feeding ecologies. Given Oncopeltus's strength as an experimental model, these new sequence resources bolster the foundation for molecular research and highlight technical considerations for the analysis of medium-sized invertebrate genomes

Visualizing Late Insect Embryogenesis: Extraembryonic and Mesodermal Enhancer Trap Expression in the Beetle Tribolium castaneum

The beetle Tribolium castaneum has increasingly become a powerful model for comparative research on insect development. One recent resource is a collection of piggyBac transposon-based enhancer trap lines. Here, we provide a detailed analysis of three selected lines and demonstrate their value for investigations in the second half of embryogenesis, which has thus far lagged behind research on early stages. Two lines, G12424 and KT650, show enhanced green fluorescent protein (EGFP) expression throughout the extraembryonic serosal tissue and in a few discrete embryonic domains. Intriguingly, both lines show for the first time a degree of regionalization within the mature serosa. However, their expression profiles illuminate distinct aspects of serosal biology: G12424 tracks the tissue's rapid maturation while KT650 expression likely reflects ongoing physiological processes. The third line, G04609, is stably expressed in mesodermal domains, including segmental muscles and the heart. Genomic mapping followed by in situ hybridization for genes near to the G04609 insertion site suggests that the transposon has trapped enhancer information for the Tribolium orthologue of midline (Tc-mid). Altogether, our analyses provide the first live imaging, long-term characterizations of enhancer traps from this collection. We show that EGFP expression is readily detected, including in heterozygote crosses that permit the simultaneous visualization of multiple tissue types. The tissue specificity provides live, endogenous marker gene expression at key developmental stages that are inaccessible for whole mount staining. Furthermore, the nonlocalized EGFP in these lines illuminates both the nucleus and cytoplasm, providing cellular resolution for morphogenesis research on processes such as dorsal closure and heart formation. In future work, identification of regulatory regions driving these enhancer traps will deepen our understanding of late developmental control, including in the extraembryonic domain, which is a hallmark of insect development but which is not yet well understood

Morphological staging definitions and values from quantitative time-lapse analyses at 30°C (see also Fig. 1).

a<p>Age is given in hours after egg lay for the minimum age from a 4-hour range, as the mean ± standard deviation. Parenthetical values for percent of development are based on a total embryogenesis period of 72 hours.</p>b<p>As the complete blastoderm stage was not quantified, we do not provide a definitive staging landmark and hence do not plot this stage in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103967#pone-0103967-g001" target="_blank">Figure 1H</a>.</p>c<p>For GBE, data were pooled from movie recordings 1 and 2. The separate values are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103967#pone-0103967-g001" target="_blank">Fig. 1H</a>.</p>d<p>Note that the staging landmark definition corresponds to the “SR” plot point in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103967#pone-0103967-g001" target="_blank">Figure 1H</a>, whereas the designations SR1–SR4 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103967#pone-0103967-g001" target="_blank">Figure 1</a> micrographs subdivide the subsequent morphological progression of serosal tissue contraction and withdrawal from the embryo.</p

Genomic mapping of the serosal lines G12424 (A) and KT650 (B).

<p>Genome browser views (version Tcas_3.0) show the insertion site (red) within the context of the entire chromosomal linkage group (ChLG, grey background) and enlarged for the region ±50 kb from the insertion site (white background). Predicted genes (Tcas_3.0 official gene set) within this region are numbered and shown in green (dark green: entire gene, light green: exons); see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103967#pone-0103967-t002" target="_blank">Tables 2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103967#pone-0103967-t003" target="_blank">3</a> for details of specific genes. Candidate genes inspected by <i>in situ</i> hybridization are labeled in blue.</p

Progression of serosal expression in the lines G12424 and KT650 across embryogenesis.

<p>Live imaging detection of GFP in the transgenic <i>Tribolium</i> lines nGFP, G12424, and KT650. Micrographs labeled with the same letter are of a single embryo. Views are ventral (A1<b>–</b>A3), lateral (A4,B,C,E,F), or dorsal (D,G), with anterior left. (<b>A–B</b>) Exemplar nGFP embryos, illustrating the uniform blastoderm (BL), primitive pit (PP, white arrowhead), closing serosal window (SW), maximum germband extension (GBE), and mid serosal rupture (SR1) stages. Dashed lines label the serosal edge (A3,B) and posterior abdomen (A4). The embryo in panels A1–4 naturally rotated to a lateral view. (<b>C–D</b>) G12424 serosal EGFP increases from shortly after serosal window closure through mid germband retraction. (<b>E</b>–<b>F1</b>) KT650 serosal EGFP increases from shortly after maximum germband extension until just before serosal rupture. Both lines exhibit early EGFP expression in yolk globules (C1,E1: dashed outlines show embryo position, SW stage). The onset of serosal EGFP expression shows an anterior-dorsal or anterior bias (C2,E2: blue lines). During germband retraction, expression in both lines becomes dynamic, with streaks of EGFP between serosal nuclei (shown for G12424: compare C3′ and C4′, dots mark selected nuclei). (<b>F2</b>–<b>G3</b>) In both lines, serosal expression persists throughout the lifetime of the tissue (shown for KT650: sequential stages following serosal rupture, SR1-4, through tissue degeneration). Percentage values (C–F1) denote normalized EGFP intensity for each line. Time stamps show embryo minimum age (at 30°C). Anatomical abbreviations: H, head; S, serosa; T(x), thoracic segment (x). Scale bars are 100 µm, except for 50 µm in C3′. (<b>H</b>) Quantification of EGFP expression time courses, showing the mean ± standard deviation, with sample sizes indicated in the legend. Asterisks mark the maximum EGFP signal. Also plotted are the durations of the two films (younger, “y”; older, “o”: grey lines) and the morphological stages defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103967#pone-0103967-t003" target="_blank">Table 3</a> (black plot points). See Methods for details.</p