Interpretable automatic detection of incomplete hippocampal inversions using anatomical criteria

Lisa Hemforth; Claire Cury; Vincent Frouin; Sylvane Desrivières; Antoine Grigis; Hugh Garavan; Rüdiger Brühl; Jean-Luc Martinot; Marie-Laure Paillère Martinot; Eric Artiges; Luise Poustka; Sarah Hohmann; Sabina Millenet; Nilakshi Vaidya; Henrik Walter; Robert Whelan; Gunter Schumann; Baptiste Couvy-Duchesne; Olivier Colliot

doi:10.1117/12.2651427

3 April 2023 Interpretable automatic detection of incomplete hippocampal inversions using anatomical criteria

Lisa Hemforth, Claire Cury, Vincent Frouin, Sylvane Desrivières, Antoine Grigis, Hugh Garavan, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Luise Poustka, Sarah Hohmann, Sabina Millenet, Nilakshi Vaidya, Henrik Walter, Robert Whelan, Gunter Schumann, Baptiste Couvy-Duchesne, Olivier Colliot

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Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 124640R (2023) https://doi.org/10.1117/12.2651427
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Incomplete Hippocampal Inversion (IHI) is an atypical anatomical pattern of the hippocampus that has been associated with several brain disorders (epilepsy, schizophrenia). IHI can be visually detected on coronal T1 weighted MRI images. IHI can be absent, partial or complete (no IHI, partial IHI, IHI). However, visual evaluation can be long and tedious, justifying the need for an automatic method. In this paper, we propose, to the best of our knowledge, the first automatic IHI detection method from T1-weighted MRI. The originality of our approach is that, instead of directly detecting IHI, we propose to predict several anatomical criteria, which each characterize a particular anatomical feature of IHI, and that can ultimately be combined for IHI detection. Such individual criteria have the advantage of providing interpretable anatomical information regarding the morphological aspect of a given hippocampus. We relied on a large population of 2,008 participants from the IMAGEN study. The approach is general and can be used with different machine learning models. In this paper, we explored two different backbone models for the prediction: a linear method (ridge regression) and a deep convolutional neural network. We demonstrated that the interpretable, anatomical based prediction was at least as good as when predicting directly the presence of IHI, while providing interpretable information to the clinician or neuroscientist. This approach may be applied to other diagnostic tasks which can be characterized radiologically by several anatomical features.

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Lisa Hemforth, Claire Cury, Vincent Frouin, Sylvane Desrivières, Antoine Grigis, Hugh Garavan, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Luise Poustka, Sarah Hohmann, Sabina Millenet, Nilakshi Vaidya, Henrik Walter, Robert Whelan, Gunter Schumann, Baptiste Couvy-Duchesne, and Olivier Colliot "Interpretable automatic detection of incomplete hippocampal inversions using anatomical criteria", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124640R (3 April 2023); https://doi.org/10.1117/12.2651427

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