# Assessing Histotaphonomy: A Pilot Study Using Image Analysis for Quantitative Scoring of Bone Diagenesis

Lolita Trenchat, Nicolas Vanderesse, Eric Pubert, Yannick Lefrais, Katrien

van de Vijver, Sacha Kacki, Eline M. J. Schotsmans

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Lolita Trenchat, Nicolas Vanderesse, Eric Pubert, Yannick Lefrais, Katrien van de Vijver, et al. "Assessing Histotaphonomy: A Pilot Study Using Image Analysis for Quantitative Scoring of Bone Diagenesis." International Journal of Osteoarchaeology, 2025, 35 (3), pp.e3404. 10.1002/oa.3404. hal-05030224

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# Assessing Histotaphonomy: A Pilot Study Using Image Analysis for Quantitative Scoring of Bone Diagenesis

Lolita Trenchat<fnref n="1" />,² ID | Nicolas Vanderesse² ID | Eric Pubert² ID | Yannick Lefrais³ | Katrien Van de Vijver⁴ ID |
Sacha Kacki²,⁵ ID | Eline M. J. Schotsmans<fnref n="1" />,² ID

**Correspondence:** Eline M. J. Schotsmans (eline@uow.edu.au)

**Received:** 26 December 2024 | **Revised:** 27 February 2025 | **Accepted:** 27 March 2025

**Funding:** This project received funding from ADERA, the Taphonomy European Network (TaphEn: CNRS IRN 0871), the European Union's Horizon 2020 research and innovation programme (grant agreement 794891), and the Australian Research Council (DE210101384) to Schotsmans.

**Keywords:** biodegradation | bone microstructure | histology | microcomputed tomography (μ-CT) | scanning electron microscopy (SEM) | taphonomy

## ABSTRACT

This pilot study focusses on improving the methodological approach of histotaphonomy by integrating image analysis software for assessing bone diagenesis in both bone histological sections and microcomputed tomography (μ-CT) scans of the same bone slice. Femurs and ribs from six individuals buried at the churchyard of St. Anne in Koekelberg (Belgium, 1833–1916) were analyzed with μ-CT and histology (light microscopy and scanning electron microscopy). Bone diagenesis was scored visually by the assessor, as well as quantitatively by image analysis. The results suggest that image analysis is an effective quantitative scoring method, eliminating subjectivity bias, specifically regarding the precision in percentage calculation of preserved areas which aids in assigning the right category of the Oxford Histological Index. However, algorithm limitations can affect the accuracy and reliability of the results, hence specialized expertise is advised. The taphonomic results show that bacterial degradation was similar within and between rib and femur of the same individual but varied between individuals depending on burial treatment (wooden versus zinc-lined coffins). Other inter-skeletal differences were caused by biological parameters such as the amount of primary lamellar bone. This study demonstrates the accuracy of quantitative scoring by advancements in technology such as image analysis, creating a basis for the incorporation of machine learning algorithms in future histotaphonomy studies. It also stresses the importance of using both histology and μ-CT as complementary methods.

## 1 | Introduction

After death, skeletal remains undergo different physical, chemical, and biological alteration, known as diagenesis (Kendall et al. 2018; Nielsen-Marsh et al. 2007; Smith et al. 2007). One of the most common diagenetic alterations is biological degradation, caused by bacteria which create microscopical foci of destruction (MFD) (Hackett 1981). While the origin of osteolytic

bacteria has long been a subject of debate, it has now been accepted that the broader depositional context has to be taken into account when making histotaphonomic interpretations (Booth et al. 2024; Schotsmans et al. 2024).

Histotaphonomy, that is, the analysis of diagenetic alterations in bone microstructure, is traditionally assessed through histology. This destructive technique allows for a 2D-restricted

<footnote n="1">¹Environmental Futures Research Centre, School of Science, University of Wollongong, Wollongong, Australia | ²PACEA, UMR 5199, CNRS, Université de Bordeaux, Ministère de la Culture, Pessac, France | ³Archéosciences Bordeaux, UMR 6034, CNRS, Université Bordeaux Montaigne, Ministère de la Culture, Pessac, France | ⁴Royal Belgian Institute of Natural Sciences, Brussels, Belgium | ⁵Department of Archaeology, Durham University, Durham, UK</footnote>

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>

<footnote>© 2025 The Author(s). *International Journal of Osteoarchaeology* published by John Wiley & Sons Ltd.</footnote>
visible light examination of bone alterations on thin sections that are ideally around 80–100 µm thickness. The most established method to score histotaphonomic alterations is the Oxford Histological Index (OHI), originally proposed by Hedges et al. (1995) and further developed by Millard (2001), using reflected light microscopy. The OHI consists of six categories ranging from 0 (no features identifiable) to 5 (well preserved), based on the percentage of bone that is unaffected by bioerosion. Over the years, other attempts were made to assess diagenesis by creating additional indices (e.g., Brönnimann et al. 2018; Hollund et al. 2012; Jans 2005; Mandl et al. 2022; White and Booth 2014). However, issues have arisen because of the unclear descriptions and non-overlapping categories (Turner-Walker 2023).

Recently, microcomputed tomography (μ-CT) has been introduced as an alternative to study bone diagenesis. In contrast to histology, μ-CT can produce microstructural information on bone volume, rather than for a single cross-section (Booth et al. 2016; Dal Sasso et al. 2014; Trenchat et al. 2024; Tripp 2018). However, analyses of diagenesis on μ-CT scans have not been standardized, nor quantified. Mandl et al. (2022) attempted to develop a semiquantitative scoring method for microdegradation, replacing the OHI by the Virtual Histological Index (VHI). They conclude that the VHI remains subjective, because it is not based on any measurable characteristics (Mandl et al. 2022). A study by Duffett Carlson et al. (2022) focussed on visualizing tunnels with μ-CT leading to a better understanding of bioerosion but did not measure bioerosion. The authors admit a high level of subjectivity by saying that “care must be taken when adjusting the colormap, as overadjustment can result in misleading visualizations” (Duffett Carlson et al. 2022).

This pilot study examines human bone samples from the 19th century churchyard of St. Anne in Brussels, Belgium, using both histology and μ-CT scanning, aiming to improve the methodological approach of histotaphonomy with quantitative scoring. The main research question explored if automated quantitative scoring through image analysis software was more accurate than visual qualitative scoring by a trained assessor. In addition, inter-skeletal, intra-skeletal, and intra-element variations were addressed.

## 2 | Materials

In 2016, the Belgian Royal Museums of Art and History (RMAH) carried out a rescue excavation of the parish churchyard of St. Anne in Koekelberg (Brussels, Belgium) that had been in used between 1833 and 1916. Historical sources indicate that the parish population was largely composed of impoverished working-class individuals with a smaller proportion of higher class families, sometimes represented by burial in zinc-lined coffins (Claes et al. 2016; Van de Vijver 2020; see Table S1 for anthropological and contextual information). For this study, six primary depositions with adults were selected based on their minimum completeness of 50%. The right femur and a right rib were targeted to investigate intra-individual and intra-element variation of diagenesis (Figures S1 and S2).

## 3 | Methods

### 3.1 | Sample Preparation

After scoring the macroscopic preservation of each bone using the Qualitative Bone Index (QBI) (Bello et al. 2003), μ-CT scans were performed on a 3-cm section of each rib (cut from 5 to 8 cm from the sternal end), the proximal third of each femur (targeting a 2-cm zone at 4 to 6 cm from the lesser trochanter; Figure 1a), and a 2×3 cm bone section cut from the latter (taken from the anterior diaphyseal femoral cortex at the same location that was previously scanned; Figure 1b). Sections of ribs and femurs were cut using a Dremel 8220 with a diamond-coated cutting wheel. Histological thin sections were then prepared from the rib and femur bone sections (see protocol below; Figure 1c).

### 3.2 | μ-CT

X-ray μ-CT scans were performed using a GE phoenix v|tome|x s scanner fitted with a 0.1 mm Cu filter. All scans were carried out at 300 μA, 100 kV, and 500-ms exposure. A total of 2550 projections were acquired over a 360° rotation with three frames averaging. The ribs were scanned at 10- to 8-μm numerical resolution. The first scans of the proximal part of the complete femurs were at 18- to 15-μm resolution, while the smaller femoral cuts had a higher resolution of 13–11 μm. 3D images were reconstructed in 16 bits with the Phoenix datos|x reconstruction 2 program.

### 3.3 | Histology

Histological thin sections of 80- to 100-μm thickness were cut from the midpoint of the scanned areas following standard histological methods (Bancroft and Stevens 1996; Miszkiewicz and Mahoney 2016; Yang et al. 2020). After embedding the sections in epoxy resin (90% Presi MA2+, 10% catalyser), they were cut with a precision saw (Buehler - Isomet 5000) with diamond blade (Buehler 11-4245). Next, the surface of the embedded blocks was manually ground with a motorized rotating grinder-polisher (Presi – “LeCube”) with three consecutive disks: a P240 disk (Presi - Reflex NAC Type M P240) and two diamond disks (Presi, iMax-R 54 μm and iMax-R 18 μm). This was followed by a polishing procedure consisting of three diamond suspensions (Presi, 500 CC diamond suspension reflex LDP 9, 3, and 1 μm) on three different pads. The sections were then mounted on glass slides combining 15 drops of epoxy resin (Escil, Geofix) and 8 drops of catalyser on a hot gluing stand (GBrot - Presse de collage 1.04.02) heated at 50°C for 1 h. Next, a level cut using the same precision saw was made. The sections were then ground and polished following the same protocol as described above. No coverslips were added to enable analysis with SEM-EDX.

### 3.4 | Analysis of Thin Sections and μ-CT Scans

The histological thin sections were analyzed using a high-powered light microscope (Leica DM2500P) with transmitted white light as well as polarized light, at magnifications of 40×, 100×, and 200× resulting in an imaging resolution of < 1 μm/pixel. Visualizations and overviews of the full thin section were
<figure><img src="image_2.png" /><figcaption>**FIGURE 1** | (a) Cross-section μ-CT scan of complete femur US27 (numerical resolution: 18 μm); (b) μ-CT scan of the fragment cut from the anteri- or part of femur US27 (red box on image a) (numerical resolution: 11 μm); (c) histological thin section of the same fragment with transmitted light mi- croscopy (numerical resolution: < 1 μm); (d–f) the same sections segmented with the Otsu algorithm (yellow: preserved bone; blue: degraded bone). [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

captured digitally with the Leica Application Suite software (Figures 1c and S3–S13).

On each thin section and corresponding μ-CT cross-section, bone diagenesis was visually scored by a trained assessor using the OHI (Hedges et al. 1995; Millard 2001).

To quantify the OHI automatically, μ-CT sections were di- rectly scored in Dragonfly (Comet Technologies Canada Inc., Montreal, Canada), under the non-commercial FreeD license, free of charge in support for academic research. For thin sec- tions, the bone was first segmented from the background on the overview images with a free image editing software (GIMP) be- fore being scored automatically in Dragonfly.

Quantitative OHI scoring was based on the discrimination between the bio-eroded bone and the well-preserved bone, the former being less dense, thus appearing darker on both thin sections and μ-CT virtual sections. The Otsu partitioning

algorithm, present in Dragonfly and many other image analysis softwares, was applied for that task, first to single out the bone from the background, then to discriminate the biodegraded bone from the well-preserved bone. The principle of this algorithm is to separate a statistical distribution into two classes with a cal- culated threshold that maximizes the interclass variance. It is known to be robust and adapted to many segmentation tasks where the intensity histogram is bimodal, which was the case for the thin sections and μ-CT images.

The result was a set of segmented images in which the altered and non-altered areas appear as distinct regions (Figures 1d–f and S3–S13). The percentage of preserved bone was easily calcu- lated by dividing the preserved area by the total area of the bone.

A weighted Cohen's Kappa test was conducted to evaluate the intra-observer reliability (trained assessor vs image analysis software). To assess the inter-rater reliability across all mea- surements, an intraclass correlation coefficient (ICC) test was
performed using a two-way mixed effect model with absolute agreement ICC (3,1) conducted with Past 4.17 software.

## 3.5 | Scanning Electron Microscopy (SEM) With Energy Dispersive X-Ray Spectroscopy (EDX)

Additionally, the thin sections were analyzed with SEM-EDX using an Ultimax 100 EDX spectrometer (Oxford Instruments) with AZTec software in a JEOL IT500 HR SEM at low vacuum mode, with a backscattered electron detector in composition mode, at 20 kV and 35 Pa.

## 4 | Results

Table 1 presents the results of the qualitative OHI scoring by the trained assessor and the quantitative OHI scoring by image analysis for both thin sections and μ-CT scans of the exact same slice. Image analysis allowed precise calculation of the percentage of preserved bone.

Regarding inter-skeletal variation, two main categories of OHI scoring were noticed: individuals US77 and US100 appeared well-preserved (OHI 4-5) while the four other individuals had a lower OHI (OHI 2-3) (Table 1; Figure 2). Interestingly, this observation does not align with the macroscopic aspect of bones, as US100 displayed friable, black cortical bone while the other bones appeared well-preserved (Table S2; Figures S1 and S2).

There was limited intra-skeletal variation (< 10%). Femoral and rib sections indicate similar percentages of preserved bone (Figure 2). In addition, the percentage of degradation remained similar over the analyzed length (Figure S14).

<figure><img src="image_3.png" /><figcaption>**FIGURE 2** | Percentage of preserved bone in femurs and ribs of every individual, calculated with the Otsu algorithm on the μ-CT scans (zinc-lined coffin is starred).</figcaption></figure>

<table><caption>**TABLE 1** | Qualitative and quantitative OHI scores of the histological thin sections and μ-CT scans of exact the same slice with additional percentages of preserved bone microstructure calculated by the image analysis software.</caption><thead><tr><th>Skeletal element</th><th>Qualitative OHI of thin sections (observer)</th><th>Qualitative OHI of micro-CT scans (observer)</th><th>Quantitative OHI of thin sections (image analysis)</th><th>Quantitative OHI of micro-CT scans (image analysis)</th><th>Additional observations</th></tr></thead><tbody><tr><td>US 15-rib</td><td>2</td><td>2</td><td>3 (58%)</td><td>3 (58%)</td><td>Primary lamellar bone</td></tr><tr><td>US 15-femur</td><td>1</td><td>2</td><td>2 (24%)</td><td>2 (45%)</td><td></td></tr><tr><td>US 27-rib</td><td>3</td><td>3</td><td>3 (68%)</td><td>3 (68%)</td><td>Primary lamellar bone</td></tr><tr><td>US 27-femur</td><td>3</td><td>3</td><td>3 (63%)</td><td>3 (63%)</td><td>Primary lamellar bone</td></tr><tr><td>US 37-rib</td><td>1</td><td>3</td><td>2 (41%)</td><td>3 (67%)</td><td></td></tr><tr><td>US 37-femur</td><td>3</td><td>3</td><td>3 (76%)</td><td>3 (76%)</td><td></td></tr><tr><td>US 76-rib</td><td>3</td><td>3</td><td>3 (62%)</td><td>3 (62%)</td><td></td></tr><tr><td>US 76-femur</td><td>3</td><td>3</td><td>3 (66%)</td><td>3 (66%)</td><td></td></tr><tr><td>US 77-rib</td><td>5</td><td>5</td><td>4 (94%)</td><td>4 (94%)</td><td>Primary lamellar bone</td></tr><tr><td>US 77-femur</td><td>5</td><td>5</td><td>5 (95%)</td><td>5 (95%)</td><td>Primary lamellar bone</td></tr><tr><td>US 100*-rib</td><td>5</td><td>5</td><td>4 (93%)</td><td>4 (93%)</td><td>Zinc infiltration</td></tr><tr><td>US 100*-femur</td><td>5</td><td>5</td><td>4 (91%)</td><td>4 (91%)</td><td>Zinc infiltration</td></tr></tbody></table>

*Note*: Discrepancies in OHI category within qualitative and within quantitative scores are underlined; discrepancies in OHI category between qualitative and quantitative scores are in bold. (*) zinc-lined coffin.
Intra-observer qualitative scores of the thin sections and μ-CT scans by the trained assessor show an *almost perfect agreement* (κ=0.82). Similarly, the quantitative scores by image analysis software showed an *almost perfect agreement* between scores of thin sections and μ-CT scans (κ=0.90) (Data S1). Looking at percentages of bone microstructure preservation, US15-femur and US37-rib show discrepancies in preservation between thin sections and μ-CT scans with US15-femur both falling into OH2 while US37-rib falls in two different OHI scorings (Table 1).

ICC (3,1) indicated an excellent agreement for individual ratings, with a value of 0.83 (95% CI: 0.6485, 0.939). For mean ratings across raters, the agreement was even stronger, with an ICC(3,k) value of 0.95 (95% CI: 0.8807, 0.984) (Data S2).

Additional observations revealed that three individuals had large amounts of primary lamellar bone (US15, US27, US77). Further characterization with SEM-EDX showed demineralised and hypermineralised areas (Figure 3) and zinc infiltrations (Table 1 and Figures S12 and S15). The hypermineralised areas displayed higher levels of calcium and phosphate compared to the demineralised ones, but the Ca/P ratio remained similar in both areas. In other words, it consisted of redeposited bone mineral.

# 5 | Discussion

## 5.1 | Advantages of Automated Quantitative Scoring of Bone Diagenesis

This pilot study confirms that image analysis is a reliable method for objectively scoring of OHI. It aligns with previous research seeking more objective approaches to analyzing bone thin sections (Duffett Carlson et al. 2022; Mandl et al. 2022) as well as

<figure><img src="image_4.png" /><figcaption>**FIGURE 3** | Backscattered SEM image of femoral section US37 showing osteons with hypermineralised (whitish-gray) and demineralised (grayish-black) areas.</figcaption></figure>

micromorphological thin sections (Zickel et al. 2024). In terms of processing time, automatic scoring of μ-CT scans was almost immediate, whereas scoring of thin sections could take 30 min to an hour due to the need for preprocessing with an image editing software. This included segmenting the bone from the background and other artifacts, such as air bubbles, to prevent these from being counted as biodegradation by the software. While a trained assessor must make a visual estimation of percentage of preserved bone and convert it to its corresponding OHI category, image analysis is more precise in estimating the area of preserved bone (Table 1). Nonetheless, the results obtained by image analysis need to be monitored, as they rely on the type of segmentation algorithm. This is demonstrated in well-preserved bone such as US77 and US100 where the percentage of degraded bone was very low. The segmentation algorithm nevertheless calculated a cut-off point (threshold) in the intensity histogram, which resulted in an overestimation of the degraded area. In these cases, the threshold needed to be manually adjusted, introducing a minor degree of subjectivity. As with all image analysis software, this reiterates that its effective use requires specialized knowledge to set parameters correctly, interpret results, and validate findings. Insufficient user expertise can lead to misinterpretation of results. Integrating machine learning algorithms could be a promising direction for future advancements.

## 5.2 | Intra-individual and Inter-individual Variation of Bone Diagenesis

From a taphonomic perspective, this study confirms that good macroscopic preservation does not necessarily correlate with good microscopic preservation and vice versa, which aligns with other histotaphonomic studies (e.g., Hanson and Buikstra 1987; Nicholson 1996). While US100 displayed low macroscopic preservation rates (QBI 1%–37%), both rib and femur appeared well-preserved on a histological level (OHI 4) (Table S2 and Figures S1 and S2). In addition, analysis with SEM-EDX confirmed observations by Turner-Walker and Syversen (2002) regarding demineralisation by bacteria and redeposition of bone mineral.

The results identified similar levels of bioerosion between rib and femur of the same individual, indicating almost no intra-skeletal variation (Table 1 and Figure 2). This contrasts with studies that found that femurs display less bioerosion than ribs (Dal Sasso et al. 2014; Hanson and Buikstra 1987). However, the small sample size of this pilot study necessitates caution in comparing results.

Between individuals, there was a high inter-skeletal variation, with US77 and US100 appearing best preserved on a microscopic level (Figure 2). US100 was buried in a zinc coffin. Analysis with SEM-EDX confirmed the presence of zinc within the bone (Figure S15). Metal containers are known to reduce aerobic decay (Pokines and Baker 2013; Schotsmans et al. 2020), and heavy metal has antibacterial effects (Mandl et al. 2018; Müller et al. 2011). Zinc, in particular, is highly soluble in acidic solutions, which might have been triggered by the organic acids from the wood decay, leading to a reduction in mineral loss at low pH (Mohammed et al. 2014; Dean et al. 2023). US100 is therefore an example that a different funerary practice could potentially be recognized on a histological level when comparing individuals from different graves.
<figure><img src="image_5.png" /><figcaption>**FIGURE 4** | Rib thin section of US27 observed with white light microscopy. A general absence of biodegradation can be observed at locations with primary lamellar bone (black rectangles). [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

The second individual with an almost immaculate histological preservation was US77 which showed a high ratio of primary lamellar bone. Similarly, individuals US15, US27 and US77 showed primary lamellar bone, in lower quantity, and had lower numbers of secondary osteons. Interestingly, upon detailed observation, bacterial degradation appeared absent in areas with primary bone and more present around the osteons (Figure 4). This might confirm that bacteria spread via the osteonal systems (Duffett Carlson et al. 2022; Hackett 1981; Trueman and Martill 2002), although more research is necessary.

## 5.3 | Limitations, Further Studies, and Conclusions

This study demonstrates the successful use of an image analysis algorithm for quantitative scoring of microscopical biodegradation in both μ-CT scan 3D images and 2D histological thin sections. However, the small sample size necessitates caution, and the application of image analysis software should be carefully considered. While image analysis removes subjectivity bias, particularly in the calculation of percentage of preserved bone microstructure, thereby improving precision, several factors may affect the accuracy and reliability of the results. These include image quality, algorithm limitations, and the need for specialized expertise. One of the limitations of this approach is that it cannot be applied without discernment when other substantial taphonomic features such as staining, chemical alterations, and sediment inclusions are at play. For example, the presence of sediment infiltrates would need a critical pre-processing step consisting of segmenting the bone material from the sediment, before applying the threshold. This confirms the importance of using both thin sections as well as μ-CT scans to obtain complementary information (e.g., Booth et al. 2016; Dal Sasso et al. 2014; Trenchat et al. 2024). Due to the difference of numerical resolution between microscopy and μ-CT, the latter does not provide as detailed a visualization of the bioerosion features as thin sections. However, μ-CT scans do allow for the distinction between bioerosion and organic infiltrates. The decrease in density caused by bioerosion could be easily visualized in the μ-CT scan images over a whole volume of bone, allowing to segment the altered zones from unaltered zones, and measure their area. Further research on larger sample sizes from diverse

depositional and environmental contexts is therefore needed to evaluate the robustness of this method. Incorporating machine learning algorithms would be the way forward.

The taphonomic results demonstrate limited intra-skeleton and intra-element variation of bone diagenesis in rib and femur in these 19th century primary burials. In contrast, inter-skeletal differences were noticeable caused by different mortuary practices (burial in wooden or zinc-lined coffin). Additional differences in bacterial attack were likely influenced by biological factors, such as the presence of primary lamellar bone (e.g., US 77). However, further investigation is needed to confirm this. These results stress the importance of knowledge about both the depositional environment and funerary practices in addition to the bio-anthropological examination of human remains.

### Acknowledgments

We would like to thank the Department of Archaeological Heritage of Urban.Brussels (Regional Public Service Brussels Urbanism and Heritage) for granting access to the skeletal collection of the cemetery of St. Anne, Koekelberg. Many thanks to Mona Le Luyer and Julie Munier for their help and advice during histological section preparation, and to Alain Queffelec for his help with archaeometric analyses. We are grateful to Justyna Miszkiewicz for her feedback on the manuscript.

### Ethics Statement

The authors have nothing to report.

### Consent

The authors have nothing to report.

### Conflicts of Interest

The authors declare no conflicts of interest.

### Data Availability Statement

All scores of thin sections and μ-CT slices are presented in this manuscript and the Supporting Information. Full scans of the sections are available upon request from the corresponding author.

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## Supporting Information

Additional supporting information can be found online in the Supporting Information section.