To my pleasant surprise, a study on medical image analysis drew my attention today that found particularly useful my earlier work on 3D shape descriptors for 3D shape matching, and in particular the work described within the paper "PANORAMA: A 3D shape descriptor based on panoramic views for unsupervised 3D object retrieval".
The respective work titled as "3D Shape Analysis of the Knee Extensor and Flexor Muscles in Patients with COPD using Mesh Projection-based Features", by Hengameh Mirzaalian, Ghassan Hamarneh, Bahareh HajGhanbari and W. Darlene Reid, used and extended the PANORAMA descriptor in order to match patients MRI of thigh muscles against a database of muscle instances.
Referring to their paper:
"In order to diﬀerentiate 4 individual thigh muscles in the healthy versus COPD groups, we ﬁrst applied a state-of-the-art 3D shape descriptor; the WT-based shape descriptor proposed by Papadakis et al.  resulting in cylindrical projections. A comparison between the classiﬁcation accuracies obtained by the aforementioned descriptors and the global shape descriptors by Ward et al.  and HajGhanbari et al.  shows that, averaged over all the 4 muscles, the WT-based shape descriptors outperformed the global shape descriptors."
"Although the presented descriptors were applied to diﬀerentiate thigh muscles, they might have a widespread application for other conditions and chronic diseases that result in muscle atrophy such as chronic heart diseases, AIDS, cancer, and osteoarthritis."
Although the development of PANORAMA aimed toward effective retrieval of generic 3D objects, it makes me proud that it could further serve the domain of medical image analysis.
I feel that this is the essence of good research, that is, to promote and develop tools and methods that assist humans and science in general in a cross-disciplinary fashion.