Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation

Many medical datasets have recently been created for medical image segmentation tasks, and it is natural to question whether we can use them to sequentially train a single model that (1) performs better on all these datasets, and (2) generalizes well and transfers better to the unknown target site domain. Prior works have achieved this goal by jointly training one model on multi-site datasets, which achieve competitive performance on average but such methods rely on the assumption about the availability of all training data, thus limiting its effectiveness in practical deployment. In this paper, we propose a novel multi-site segmentation framework called incremental-transfer learning (ITL), which learns a model from multi-site datasets in an end-to-end sequential fashion. Specifically, "incremental" refers to training sequentially constructed datasets, and "transfer" is achieved by leveraging useful information from the linear combination of embedding features on each dataset. In addition, we introduce our ITL framework, where we train the network including a site-agnostic encoder with pre-trained weights and at most two segmentation decoder heads. We also design a novel site-level incremental loss in order to generalize well on the target domain. Second, we show for the first time that leveraging our ITL training scheme is able to alleviate challenging catastrophic forgetting problems in incremental learning. We conduct experiments using five challenging benchmark datasets to validate the effectiveness of our incremental-transfer learning approach. Our approach makes minimal assumptions on computation resources and domain-specific expertise, and hence constitutes a strong starting point in multi-site medical image segmentation

Integrated analysis of anatomical and electrophysiological human intracranial data

Human intracranial electroencephalography (iEEG) recordings provide data with much greater spatiotemporal precision than is possible from data obtained using scalp EEG, magnetoencephalography (MEG), or functional MRI. Until recently, the fusion of anatomical data (MRI and computed tomography (CT) images) with electrophysiological data and their subsequent analysis have required the use of technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that enables complex raw human iEEG data to be converted into more readily comprehensible illustrative representations. The protocol uses an open-source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and used by a large research community. In this protocol, we describe how to analyze complex iEEG datasets by providing an intuitive and rapid approach that can handle both neuroanatomical information and large electrophysiological datasets. We provide a worked example using an example dataset. We also explain how to automate the protocol and adjust the settings to enable analysis of iEEG datasets with other characteristics. The protocol can be implemented by a graduate student or postdoctoral fellow with minimal MATLAB experience and takes approximately an hour to execute, excluding the automated cortical surface extraction

Novel technique to fenestrate an aortic dissection flap using electrocautery

Chronic distal thoracic dissections treated with thoracic endovascular repair are prone to type Ib false lumen perfusion. When the supraceliac aorta is of normal caliber, fenestration of the dissection flap proximal to the visceral vessels creates a seal zone for the thoracic stent graft and eliminates the type Ib false lumen perfusion. We describe a novel way of crossing the septum using electrocautery delivered through a wire tip then fenestrating the septum using electrocautery delivered over a 1-mm area of uninsulated wire to cut the septum. We believe the use of electrocautery creates a controlled and deliberate aortic fenestration during endovascular repair of a distal thoracic dissections

Novel technique to fenestrate an aortic dissection flap using electrocautery

Chronic distal thoracic dissections treated with thoracic endovascular repair are prone to type Ib false lumen perfusion. When the supraceliac aorta is of normal caliber, fenestration of the dissection flap proximal to the visceral vessels creates a seal zone for the thoracic stent graft and eliminates the type Ib false lumen perfusion. We describe a novel way of crossing the septum using electrocautery delivered through a wire tip then fenestrating the septum using electrocautery delivered over a 1-mm area of uninsulated wire to cut the septum. We believe the use of electrocautery creates a controlled and deliberate aortic fenestration during endovascular repair of a distal thoracic dissections

Weakly Supervised Deep Learning for Aortic Valve Finite Element Mesh Generation from 3D CT Images

Finite Element Analysis (FEA) is useful for simulating Transcather Aortic Valve Replacement (TAVR), but has a significant bottleneck at input mesh generation. Existing automated methods for imaging-based valve modeling often make heavy assumptions about imaging characteristics and/or output mesh topology, limiting their adaptability. In this work, we propose a deep learning-based deformation strategy for producing aortic valve FE meshes from noisy 3D CT scans of TAVR patients. In particular, we propose a novel image analysis problem formulation that allows for training of mesh prediction models using segmentation labels (i.e. weak supervision), and identify a unique set of losses that improve model performance within this framework. Our method can handle images with large amounts of calcification and low contrast, and is compatible with predicting both surface and volumetric meshes. The predicted meshes have good surface and correspondence accuracy, and produce reasonable FEA results

Short-Term Follow-up of Endovascular Electrocautery Septostomy and Fenestration for Distal Landing Zone Optimization in Chronic Distal Aortic Dissections

Objectives: Endovascular repair of postdissection thoracic aneurysms remains challenging due to false lumen perfusion. Landing the endograft into the true lumen renders the repair prone to false lumen perfusion through fenestrations beyond to the distal landing zone. Fenstration of the septum creating a single lumen at the distal landing zone is one possible strategy to eliminate false lumen perfusion. We describe our experience with endovascular electrocautery septostomy and fenestration. Methods: Patients with chronic distal aortic dissection who underwent endovascular electrocautery septostomy and fenestration followed by thoracic endovascular aneurysm repair were reviewed. The dissections were either a chronic type B dissection or a chronic residual type A dissection after proximal repair. Patient demographics, history, aortic characteristics, operative, and postoperative variables were collected. Results: Between 2019 and 2022, 13 patients underwent thoracic endovascular aneurysm repair with endovascular electrocautery fenestration of the distal dissection flap to facilitate the distal seal. The average age was 60 years and 11 (85%) were men. The descending thoracic aneurysm was secondary to chronic residual dissection after prior type A repair in 11 (85%) and chronic type B dissections in 2 (15%). Median time from the initial dissection was 3.6 years. Initial technical success was achieved in 12 of 13 cases (Figures 1 and 2). Average fluoroscopy time was 57.5 minutes. One patient had a persistent type Ib endoleak after the graft failed to fully expand, despite fenestration, and underwent coiling of the false lumen. Median follow-up was 7 months. Two patients developed distal aortic aneurysmal degeneration and underwent distal extension of the endograft with endoanchors. Mean time to degeneration was 41.5 weeks. Average decrease in aneurysm size since operation was 5 mm. No patient required an open operation. Conclusions: Endovascular electrocautery fenestration is a useful strategy for creating a distal landing zone in chronic descending aortic dissections. Longer term follow-up is needed to determine the longevity of the distal seal aided by septal fenestration

Acute Limb Ischemia: Patient-reported Quality of Life and Ambulation Outcomes

Objectives: There are few studies describing quality of life (QoL) and ambulation status after acute limb ischemia (ALI). We used a vascular disease-specific questionnaire (VascuQoL-6) and a generic quality of life assessment (European Quality of Life 5D-5L [EQ-5D]) to assess these outcomes. Methods: Using a prospectively collected, single-institution ALI database, the EQ-5D and VascuQoL-6 surveys were administered. Patient demographics, medical history, inpatient variables, outcomes, and ambulatory functional status at last follow-up were collected. Univariate analyses were used to correlate the VascuQoL-6 composite score and the EQ-5D index score with the collected variables. Results: Between May 2016 and February 2022, 234 patients were entered into the database; of these, 40 responded to our surveys (17%). Average age was 59 years, 55% were male, and 45% were Black. Rutherford class on presentation was 1 in 10 patients, 2a in 11 patients, 2b in 17 patients, and 3 in two patients. Three patients underwent medical management only, four patients had a primary amputation, 10 patients underwent endovascular revascularization, and 22 patients underwent an open revascularization. At 30 days, 93% of patients (37/40) had limb salvage; however, by 1 year, this decreased to 60% (22/37). Functional status at last follow-up (mean, 15-18 months) included 23 patients with normal ambulation, 10 patients with partially limited ambulation (neurological deficit or chronic pain), five ambulatory on prosthetics after amputation, and two non-ambulatory after amputation. Average VascuQoL-6 score was 16.8 (of a max of 24) for normal ambulation, 13.8 for partially limited ambulation, and 15.8 for prosthetic ambulation after amputation (P-value =.223). The VascuQoL-6 composite score was associated with age (inversely correlated; P \u3c.001) and EQ-5D score (P \u3c.001) only. The EQ-5D was also significant for age (inversely correlated; P =.032) and VascuQoL-6 composite score (P \u3c.001), whereas ambulatory functional status approached significance (P =.079). Rutherford classification, etiology, type of revascularization, length of stay, limb salvage, and functional ambulatory status did not correlate with QoL outcomes on either assessment. Conclusions: When comparing QoL after acute limb ischemia, younger patients had worse functional outcomes. There was no statistically significant difference in QoL for presenting Rutherford classification, limb salvage, type of revascularization, or functional ambulatory status