iDermatoPath – A novel software tool for mitosis detection in H&E stained tissue sections of malignant melanoma

Abstract

Background

Malignant Melanoma (MM) is characterized by a growing incidence and a high malignant potential. Beside well defined prognostic factors such as tumor thickness and ulceration, the Mitotic Rate (MR) was included in the AJCC recommendations for diagnosis and treatment of MM. In daily routine, the identification of a single mitosis can be difficult on Hematoxylin&Eosin slides alone. Several studies showed a big inter- and intra-individual variability in detecting the MR in MM even by very experienced investigators, thus raising the question for a computer assisted method.

Objective

The objective was to develop a software system for mitosis detection in MM on H&E slides based on machine learning for diagnostic support.

Methods

We developed a computer-aided staging support system based on image analysis and machine learning on the basis of 59 MM specimens. Our approach automatically detects tumor regions, identifies mitotic nuclei, and classifies them with respect to their diagnostic relevance. A convenient user interface enables the investigator to browse through the proposed mitoses for fast and efficient diagnosing.

Results

A quantitative evaluation on manually labelled ground truth data revealed that the tumor region detection yields a medium spatial overlap index (dice coefficient) of 0.72. For the mitosis detection we obtained high accuracies of above 83%.

Conclusion

On the technical side, the developed iDermatoPath software tool provides a novel approach for mitosis detection in MM, which can be further improved by using more training data such as dermatopathologist annotations. On the practical side, a first evaluation of the clinical utility was positive, albeit this approach provides most benefit for difficult cases in a research setting. Assuming all slides to be digitally processed and reported in the near future, this method could become a helpful additional tool for the pathologist.

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